Analyses on Enrichment Microcosms

Introduction

In addition to SIP, we also ran a simple enrichment experiment, adding one 12C substrate to smaller microcoms containing ~ 5g soil. Unlike the SIP experiment, only one substrate was added to each microcosm and this time we used soil from all 10 locations, not just Monkey Run. Also we only sampled microcoms on two days for each substrate:

• Days 2 and 4: xylose, amino acids, and vanillin
• Days 14 and 28: cellulose and palmitic acid

We also had water treated controls for each timepoint as something to compare against.

In this notebook I will examine these samples both on their own and in relation to the incorporators identified in the MW-HR-SIP analysis.

Initialization

# For data handling
library(dplyr)
library(phyloseq)

# For analysis
library(vegan)
library(nlme)
library(lsmeans)

# For plotting
library(ggplot2)

# Set color schemes
eco.col = c(agriculture="#00BA38", meadow="#619CFF", forest="#F8766D")

g_legend<-function(a.gplot){
  tmp <- ggplot_gtable(ggplot_build(a.gplot))
  leg <- which(sapply(tmp$grobs, function(x) x$name) == "guide-box")
  legend <- tmp$grobs[[leg]]
  return(legend)}

Data

The OTU abundances and phylogeny can be found in the master phyloseq object.

# Import phyloseq object
physeq = readRDS("/Users/sambarnett/Documents/Buckley Lab/FullCyc2/fullcyc2_backups_8_8_19/phyloseq/fullcyc2physeq.RDS")

# Subset to just the unfractionated samples and remove the controls
enr.physeq = subset_samples(physeq, exp_type == "Enrichment" & sample_type == "unknown")
bulk.physeq = subset_samples(physeq, exp_type == "bulk" & sample_type == "unknown")
physeq = NULL

# Remove non-bacteria (aka Archaea)
enr.physeq = subset_taxa(enr.physeq, Domain == "Bacteria")
bulk.physeq = subset_taxa(bulk.physeq, Domain == "Bacteria")
  
# Add in a different phylogenetic tree. The one in the phyloseq might be an older version.
tree = read_tree("/Users/sambarnett/Documents/Buckley Lab/FullCyc2/fullcyc2_backups_8_8_19/fullcyc2.bacteria.cogent.tree")
phy_tree(enr.physeq) = tree
phy_tree(bulk.physeq) = tree

# Remove any OTUs no longer found in the samples
enr.physeq = prune_taxa(taxa_sums(enr.physeq) > 0, enr.physeq)
bulk.physeq = prune_taxa(taxa_sums(bulk.physeq) > 0, bulk.physeq)

Incorporator status can be found in the log2 fold change dataframe.

# Get the l2fc dataframe and add in columns indicating the land-use, labeled substrate, and day
l2fc.df = readRDS(file = "/Users/sambarnett/Documents/Buckley Lab/FullCyc2/fullcyc2_l2fc_testoutput.rds") %>%
  mutate(ecosystem  = factor(gsub(".+ecosystem == [ \']*([A-z]+).+", "\\1", .id),
                             levels = c("agriculture", "meadow", "forest")),
         day  = gsub(".+day == [ \']*([0-9]+).+", "\\1", .id),
         substrate = factor(gsub(".+(13C-[A-z]+).+", "\\1", .id),
                            levels = c("13C-Xyl", "13C-Ami", "13C-Van", "13C-Cel", "13C-Pal")))

For many of the following analyses I want a rarefied OTU table. This is one way to correct for differing sequencing depths across all my samples. I will set the seed for this process so that I can replicate this analysis if necessary (seed = 4242).

enr.rare.physeq = rarefy_even_depth(enr.physeq, rngseed=4242)
bulk.rare.physeq = rarefy_even_depth(bulk.physeq, rngseed=4242)

unique(colSums(otu_table(enr.rare.physeq)))

## [1] 14503

Basic stats

print(paste("Maximum read count =", max(colSums(otu_table(enr.physeq)))))

## [1] "Maximum read count = 194737"

print(paste("Minimum read count =", min(colSums(otu_table(enr.physeq)))))

## [1] "Minimum read count = 14503"

print(paste("Rarefied read count =", unique(colSums(otu_table(enr.rare.physeq)))))

## [1] "Rarefied read count = 14503"

print(paste("Number of OTUs total =", ntaxa(enr.physeq)))

## [1] "Number of OTUs total = 28296"

print(paste("Number of OTUs rarefied =", ntaxa(enr.rare.physeq)))

## [1] "Number of OTUs rarefied = 24659"

print(paste("Number of phyla total =", length(unique(filter(data.frame(tax_table(enr.physeq), stringsAsFactors = FALSE), !(is.na(Phylum)))$Phylum))))

## [1] "Number of phyla total = 45"

print(paste("Number of phyla total =", length(unique(filter(data.frame(tax_table(enr.rare.physeq), stringsAsFactors = FALSE), !(is.na(Phylum)))$Phylum))))

## [1] "Number of phyla total = 45"

Diversity differences between treatment and control microcosms

As with the SIP enrichments I want to see if there carbon addition leads to a shift in diversity.

Here I’ll calculate the desired alpha diveristy measures.

OTU.table = t(otu_table(enr.rare.physeq))
alpha_div.df = data.frame(X.Sample = rownames(OTU.table), 
                          richness = specnumber(OTU.table),
                          shannon = diversity(OTU.table, index="shannon"),
                          simpson = diversity(OTU.table, index="simpson")) %>%
  mutate(evenness = shannon/log(richness)) %>%
  left_join(data.frame(sample_data(enr.rare.physeq)) %>% select(X.Sample, ecosystem, substrate, day, location),
            by = "X.Sample") %>%
  mutate(ecosystem = factor(ecosystem, levels = c("agriculture", "meadow", "forest")),
         substrate = factor(substrate, levels = c("H2O-Con", "12C-Xyl", "12C-Ami", "12C-Van", "12C-Cel", "12C-Pal")),
         time_point = ifelse(day %in% c(2, 14), "Early", "Late")) %>%
  tidyr::gather(key="method", value="Treatment", 
                -X.Sample, -ecosystem, -substrate, -day, -location, -time_point)

alpha_div_H2O.df = alpha_div.df %>%
  filter(substrate == "H2O-Con") %>%
  rename(Control = Treatment) %>%
  select(ecosystem, location, day, method, Control)

alpha_div_treat.df = alpha_div.df %>%
  filter(substrate != "H2O-Con") %>%
  select(-X.Sample) %>%
  left_join(alpha_div_H2O.df, by = c("ecosystem", "location", "day", "method")) %>%
  tidyr::gather(key = "treatment", value = "diversity", -ecosystem, -substrate, -day, -location, -time_point, -method) %>%
  arrange(ecosystem, substrate, day, location)

Early timepoints

Early timepoints are the first timepoint for each of the substrates (Day 2 for Xylose, amino acids, and vanillin; Day 14 for palmitic acid and cellulose). This is similar to the early timepoints for SIP and likely captures bacterial growth due to substrate addition more so than secondary feeding.

## Get bulk
bulk_meta.df = data.frame(sample_data(bulk.rare.physeq)) %>%
  select(location, ecosystem, pH, organic_content_perc, DNA_conc__ng_ul) %>%
  mutate(DNA_conc__ng_ul = as.numeric(as.character(DNA_conc__ng_ul)))

evenness_early_meta.df = alpha_div_treat.df %>%
  filter(method == "evenness", time_point == "Early") %>%
  tidyr::spread(key=treatment, value=diversity) %>%
  mutate(delta_evenness = Treatment-Control) %>%
  left_join(bulk_meta.df, by = c("ecosystem", "location")) %>%
  mutate(ecosystem = factor(ecosystem, levels=c("agriculture", "meadow", "forest")))

## Compare to zero
early_evenness_wilcox.df = data.frame()
for (sub in c("12C-Xyl", "12C-Ami", "12C-Van", "12C-Cel", "12C-Pal")){
  model = wilcox.test(x = filter(evenness_early_meta.df, substrate==sub)$delta_evenness, alternative = "less", mu=0)
  model.df = data.frame(substrate = factor(sub, levels=c("12C-Xyl", "12C-Ami", "12C-Van", "12C-Cel", "12C-Pal")),
                        Vstat = model$statistic,
                        pvalue = model$p.value)
  early_evenness_wilcox.df = rbind(early_evenness_wilcox.df, model.df)
}

early_evenness_wilcox.df = early_evenness_wilcox.df %>%
  mutate(padj = p.adjust(pvalue, method = "BH", n = 5)) %>%
  mutate(sig = ifelse(padj < 0.001, "***",
                      ifelse(padj < 0.01, "**",
                             ifelse(padj < 0.05, "*", "NS"))))

early_evenness_wilcox.plot = ggplot(data=evenness_early_meta.df, aes(x=substrate, y=delta_evenness)) +
  geom_hline(yintercept = 0, linetype=2, color="red") +
  geom_boxplot(outlier.shape = NA) +
  geom_jitter(aes(color=ecosystem), alpha=0.5) +
  geom_text(data=filter(early_evenness_wilcox.df, padj < 0.05), aes(x=substrate, label=sig), y=0.06, size=6) +
  scale_color_manual(values = eco.col, labels = c("agriculture" = "Cropland", "meadow" = "Old-Field", "forest" = "Forest")) +
  labs(x="Substrate", y="Change in evenness", color="Land-use") +
  lims(y=c(-0.3, 0.1)) +
  theme_bw() + 
  theme(axis.text.x = element_text(size=12, angle=45, vjust=1, hjust=1),
        axis.text.y = element_text(size=12),
        axis.title = element_text(size=12),
        strip.text = element_text(size=12),
        legend.text = element_text(size=12),
        legend.title = element_text(size=12),
        legend.position = "top")


## Compare with organic content
early_SOM_evenness.df = data.frame()
for (sub in c("12C-Xyl", "12C-Ami", "12C-Van", "12C-Cel", "12C-Pal")){
  sub.evenness.df = filter(evenness_early_meta.df, substrate==sub)
  cor.res = cor.test(x=sub.evenness.df$organic_content_perc, y=sub.evenness.df$delta_evenness)
  model.df = data.frame(substrate = factor(sub, levels=c("12C-Xyl", "12C-Ami", "12C-Van", "12C-Cel", "12C-Pal")),
                        r = cor.res$estimate,
                        pvalue = cor.res$p.value)  %>%
    mutate(padj = p.adjust(pvalue, method = "BH", n = 5))
  early_SOM_evenness.df = rbind(early_SOM_evenness.df, model.df)
}

early_evenness_SOM.plot = ggplot(data=evenness_early_meta.df, aes(x=organic_content_perc, y=delta_evenness)) +
  geom_smooth(method="lm", se=FALSE, color="grey70", size=0.75) +
  geom_point(aes(color=ecosystem)) +
  geom_text(data=early_SOM_evenness.df, aes(label=paste("r==", round(r, 3), sep="")),
            x=0.03, y=-0.2, hjust=0, parse = TRUE) +
  geom_text(data=early_SOM_evenness.df, aes(label=paste("p==", round(padj, 3), sep="")),
            x=0.03, y=-0.235, hjust=0, parse = TRUE) +
  scale_color_manual(values = eco.col, labels = c("agriculture" = "Cropland", "meadow" = "Old-Field", "forest" = "Forest")) +
  lims(y=c(-0.3, 0.1)) +
  labs(x="% SOM in bulk soil", y="Change in evenness", color="Land-use") +
  theme_bw() + 
  theme(axis.text.x = element_text(size=12, angle=90, vjust=0.5),
        axis.text.y = element_text(size=12),
        axis.title = element_text(size=12),
        strip.text = element_text(size=12),
        legend.text = element_text(size=12),
        legend.title = element_text(size=12),
        legend.position = "top") +
  facet_wrap(~factor(gsub("12C-", "", substrate), levels=c("Xyl", "Ami", "Van", "Cel", "Pal")), nrow=1)

## Compare with DNA concentration
early_DNA_evenness.df = data.frame()
for (sub in c("12C-Xyl", "12C-Ami", "12C-Van", "12C-Cel", "12C-Pal")){
  sub.evenness.df = filter(evenness_early_meta.df, substrate==sub)
  cor.res = cor.test(x=sub.evenness.df$DNA_conc__ng_ul, y=sub.evenness.df$delta_evenness)
  model.df = data.frame(substrate = factor(sub, levels=c("12C-Xyl", "12C-Ami", "12C-Van", "12C-Cel", "12C-Pal")),
                        r = cor.res$estimate,
                        pvalue = cor.res$p.value)  %>%
    mutate(padj = p.adjust(pvalue, method = "BH", n = 5))
  early_DNA_evenness.df = rbind(early_DNA_evenness.df, model.df)
}

early_evenness_DNA.plot = ggplot(data=evenness_early_meta.df, aes(x=DNA_conc__ng_ul, y=delta_evenness)) +
  geom_smooth(method="lm", se=FALSE, color="grey70", size=0.75) +
  geom_point(aes(color=ecosystem)) +
  geom_text(data=early_DNA_evenness.df, aes(label=paste("r==", round(r, 3), sep="")),
            x=5, y=-0.2, hjust=0, parse = TRUE) +
  geom_text(data=early_DNA_evenness.df, aes(label=paste("p==", round(padj, 3), sep="")),
            x=5, y=-0.235, hjust=0, parse = TRUE) +
  scale_color_manual(values = eco.col, labels = c("agriculture" = "Cropland", "meadow" = "Old-Field", "forest" = "Forest")) +
  lims(y=c(-0.3, 0.1)) +
  labs(x="DNA Concentration", y="Change in evenness", color="Land-use") +
  theme_bw() + 
  theme(axis.text.x = element_text(size=12, angle=90, vjust=0.5),
        axis.text.y = element_text(size=12),
        axis.title = element_text(size=12),
        strip.text = element_text(size=12),
        legend.text = element_text(size=12),
        legend.title = element_text(size=12),
        legend.position = "top") +
  facet_wrap(~factor(gsub("12C-", "", substrate), levels=c("Xyl", "Ami", "Van", "Cel", "Pal")), nrow=1)

## Plot together

early_landuse.leg = g_legend(early_evenness_SOM.plot + theme(legend.direction = "vertical", 
                                                             legend.box.background = element_rect(colour = "black")))

early_alpha_SOM.plot = cowplot::plot_grid(early_evenness_wilcox.plot + theme(legend.position = "none"),
                                          early_evenness_SOM.plot + theme(legend.position = "none", axis.title.y = element_blank()),
                                          early_landuse.leg,
                                          early_evenness_DNA.plot + theme(legend.position = "none", axis.title.y = element_blank()), 
                                          ncol=2, labels=c("A", "B", "", "C"), rel_widths = c(0.5, 1, 0.5, 1))

early_alpha_SOM.plot

early_evenness_wilcox.df

##    substrate Vstat       pvalue         padj sig
## V    12C-Xyl   177 1.310611e-01 2.098336e-01  NS
## V1   12C-Ami    70 2.300534e-04 5.751336e-04 ***
## V2   12C-Van    15 1.275912e-07 6.379560e-07 ***
## V3   12C-Cel   210 3.277192e-01 3.277192e-01  NS
## V4   12C-Pal   172 1.678669e-01 2.098336e-01  NS

early_SOM_evenness.df

##      substrate         r      pvalue       padj
## cor    12C-Xyl 0.4377734 0.015545185 0.07772593
## cor1   12C-Ami 0.4648663 0.009646017 0.04823009
## cor2   12C-Van 0.3574692 0.052456283 0.26228141
## cor3   12C-Cel 0.3947730 0.030854962 0.15427481
## cor4   12C-Pal 0.1486803 0.441452927 1.00000000

early_DNA_evenness.df

##      substrate         r       pvalue        padj
## cor    12C-Xyl 0.4805024 7.197024e-03 0.035985118
## cor1   12C-Ami 0.6554694 8.442419e-05 0.000422121
## cor2   12C-Van 0.5498244 1.647087e-03 0.008235434
## cor3   12C-Cel 0.3027813 1.038712e-01 0.519355835
## cor4   12C-Pal 0.1253467 5.170601e-01 1.000000000

Late timepoints

Late timepoints are the second timepoint for each of the substrates (Day 4 for Xylose, amino acids, and vanillin; Day 28 for palmitic acid and cellulose). This is similar to the late timepoints for SIP and likely captures bacterial growth due more to secondary feeding and late feeding on substrates.

## Get bulk
bulk_meta.df = data.frame(sample_data(bulk.rare.physeq)) %>%
  select(location, ecosystem, pH, organic_content_perc, DNA_conc__ng_ul) %>%
  mutate(DNA_conc__ng_ul = as.numeric(as.character(DNA_conc__ng_ul)))

evenness_late_meta.df = alpha_div_treat.df %>%
  filter(method == "evenness", time_point == "Late") %>%
  tidyr::spread(key=treatment, value=diversity) %>%
  mutate(delta_evenness = Treatment-Control) %>%
  left_join(bulk_meta.df, by = c("ecosystem", "location")) %>%
  mutate(ecosystem = factor(ecosystem, levels=c("agriculture", "meadow", "forest")))

## Compare to zero
late_evenness_wilcox.df = data.frame()
for (sub in c("12C-Xyl", "12C-Ami", "12C-Van", "12C-Cel", "12C-Pal")){
  model = wilcox.test(x = filter(evenness_late_meta.df, substrate==sub)$delta_evenness, alternative = "less", mu=0)
  model.df = data.frame(substrate = factor(sub, levels=c("12C-Xyl", "12C-Ami", "12C-Van", "12C-Cel", "12C-Pal")),
                        Vstat = model$statistic,
                        pvalue = model$p.value)
  late_evenness_wilcox.df = rbind(late_evenness_wilcox.df, model.df)
}

late_evenness_wilcox.df = late_evenness_wilcox.df %>%
  mutate(padj = p.adjust(pvalue, method = "BH", n = 5)) %>%
  mutate(sig = ifelse(padj < 0.001, "***",
                      ifelse(padj < 0.01, "**",
                             ifelse(padj < 0.05, "*", "NS"))))

late_evenness_wilcox.plot = ggplot(data=evenness_late_meta.df, aes(x=substrate, y=delta_evenness)) +
  geom_hline(yintercept = 0, linetype=2, color="red") +
  geom_boxplot(outlier.shape = NA) +
  geom_jitter(aes(color=ecosystem), alpha=0.5) +
  geom_text(data=filter(late_evenness_wilcox.df, padj < 0.05), aes(x=substrate, label=sig), y=0.06, size=6) +
  scale_color_manual(values = eco.col, labels = c("agriculture" = "Cropland", "meadow" = "Old-Field", "forest" = "Forest")) +
  labs(x="Substrate", y="Change in evenness", color="Land-use") +
  lims(y=c(-0.3, 0.1)) +
  theme_bw() + 
  theme(axis.text.x = element_text(size=12, angle=45, vjust=1, hjust=1),
        axis.text.y = element_text(size=12),
        axis.title = element_text(size=12),
        strip.text = element_text(size=12),
        legend.text = element_text(size=12),
        legend.title = element_text(size=12),
        legend.position = "top")


## Compare with organic content

late_SOM_evenness.df = data.frame()
for (sub in c("12C-Xyl", "12C-Ami", "12C-Van", "12C-Cel", "12C-Pal")){
  sub.evenness.df = filter(evenness_late_meta.df, substrate==sub)
  cor.res = cor.test(x=sub.evenness.df$organic_content_perc, y=sub.evenness.df$delta_evenness)
  model.df = data.frame(substrate = factor(sub, levels=c("12C-Xyl", "12C-Ami", "12C-Van", "12C-Cel", "12C-Pal")),
                        r = cor.res$estimate,
                        pvalue = cor.res$p.value)  %>%
    mutate(padj = p.adjust(pvalue, method = "BH", n = 5))
  late_SOM_evenness.df = rbind(late_SOM_evenness.df, model.df)
}

late_evenness_SOM.plot = ggplot(data=evenness_late_meta.df, aes(x=organic_content_perc, y=delta_evenness)) +
  geom_smooth(method="lm", se=FALSE, color="grey70", size=0.75) +
  geom_point(aes(color=ecosystem)) +
  geom_text(data=late_SOM_evenness.df, aes(label=paste("r==", round(r, 3), sep="")),
            x=0.03, y=-0.2, hjust=0, parse = TRUE) +
  geom_text(data=late_SOM_evenness.df, aes(label=paste("p==", round(padj, 3), sep="")),
            x=0.03, y=-0.235, hjust=0, parse = TRUE) +
  scale_color_manual(values = eco.col, labels = c("agriculture" = "Cropland", "meadow" = "Old-Field", "forest" = "Forest")) +
  lims(y=c(-0.3, 0.1)) +
  labs(x="% SOM in bulk soil", y="Change in evenness", color="Land-use") +
  theme_bw() + 
  theme(axis.text.x = element_text(size=12, angle=90, vjust=0.5),
        axis.text.y = element_text(size=12),
        axis.title = element_text(size=12),
        strip.text = element_text(size=12),
        legend.text = element_text(size=12),
        legend.title = element_text(size=12),
        legend.position = "top") +
  facet_wrap(~factor(gsub("12C-", "", substrate), levels=c("Xyl", "Ami", "Van", "Cel", "Pal")), nrow=1)

## Compare with DNA concentration
late_DNA_evenness.df = data.frame()
for (sub in c("12C-Xyl", "12C-Ami", "12C-Van", "12C-Cel", "12C-Pal")){
  sub.evenness.df = filter(evenness_late_meta.df, substrate==sub)
  cor.res = cor.test(x=sub.evenness.df$DNA_conc__ng_ul, y=sub.evenness.df$delta_evenness)
  model.df = data.frame(substrate = factor(sub, levels=c("12C-Xyl", "12C-Ami", "12C-Van", "12C-Cel", "12C-Pal")),
                        r = cor.res$estimate,
                        pvalue = cor.res$p.value)  %>%
    mutate(padj = p.adjust(pvalue, method = "BH", n = 5))
  late_DNA_evenness.df = rbind(late_DNA_evenness.df, model.df)
}

late_evenness_DNA.plot = ggplot(data=evenness_late_meta.df, aes(x=DNA_conc__ng_ul, y=delta_evenness)) +
  geom_smooth(method="lm", se=FALSE, color="grey70", size=0.75) +
  geom_point(aes(color=ecosystem)) +
  geom_text(data=late_DNA_evenness.df, aes(label=paste("r==", round(r, 3), sep="")),
            x=3, y=-0.2, hjust=0, parse = TRUE) +
  geom_text(data=late_DNA_evenness.df, aes(label=paste("p==", round(padj, 3), sep="")),
            x=3, y=-0.235, hjust=0, parse = TRUE) +
  scale_color_manual(values = eco.col, labels = c("agriculture" = "Cropland", "meadow" = "Old-Field", "forest" = "Forest")) +
  lims(y=c(-0.3, 0.1)) +
  labs(x="DNA Concentration", y="Change in evenness", color="Land-use") +
  theme_bw() + 
  theme(axis.text.x = element_text(size=12, angle=90, vjust=0.5),
        axis.text.y = element_text(size=12),
        axis.title = element_text(size=12),
        strip.text = element_text(size=12),
        legend.text = element_text(size=12),
        legend.title = element_text(size=12),
        legend.position = "top") +
  facet_wrap(~factor(gsub("12C-", "", substrate), levels=c("Xyl", "Ami", "Van", "Cel", "Pal")), nrow=1)

## Plot together

late_landuse.leg = g_legend(late_evenness_SOM.plot + theme(legend.direction = "vertical", 
                                                           legend.box.background = element_rect(colour = "black")))

cowplot::plot_grid(late_evenness_wilcox.plot + theme(legend.position = "none"),
                   late_evenness_SOM.plot + theme(legend.position = "none", axis.title.y = element_blank()),
                   late_landuse.leg,
                   late_evenness_DNA.plot + theme(legend.position = "none", axis.title.y = element_blank()), 
                   ncol=2, labels=c("A", "B", "", "C"), rel_widths = c(0.5, 1, 0.5, 1))

late_evenness_wilcox.df

##    substrate Vstat       pvalue         padj sig
## V    12C-Xyl   215 3.651710e-01 6.086183e-01  NS
## V1   12C-Ami    98 2.332402e-03 5.831006e-03  **
## V2   12C-Van    18 2.356246e-07 1.178123e-06 ***
## V3   12C-Cel   230 6.090689e-01 7.526750e-01  NS
## V4   12C-Pal   249 7.526750e-01 7.526750e-01  NS

late_SOM_evenness.df

##      substrate           r       pvalue        padj
## cor    12C-Xyl  0.05516903 0.7721575116 1.000000000
## cor1   12C-Ami  0.41074059 0.0241539493 0.120769746
## cor2   12C-Van  0.58126223 0.0007557696 0.003778848
## cor3   12C-Cel -0.33922920 0.0718198662 0.359099331
## cor4   12C-Pal -0.05088678 0.7932062614 1.000000000

late_DNA_evenness.df

##      substrate            r       pvalue        padj
## cor    12C-Xyl  0.026575752 0.8891335818 1.000000000
## cor1   12C-Ami  0.383383545 0.0364996598 0.182498299
## cor2   12C-Van  0.585679112 0.0006731203 0.003365602
## cor3   12C-Cel -0.194922309 0.3109276590 1.000000000
## cor4   12C-Pal  0.005407977 0.9777882112 1.000000000

Community composition difference between treatment and control microcosms

Since these are enrichments we cannot identify specific OTUs that have taken up a given substrate, however we can look at the community level change due to the substrate by comparing the treament microcosms to their corresponding water controls.

The first thing I need to do is measure the distance or dissimilarity between all microcosm communities. I will use three metrics for this: Bray-Curtis dissimilarity, unweighted UniFrac distance, and weighted UniFrac.

enr_BC.dist = vegdist(t(otu_table(enr.rare.physeq)), method="bray", binary=FALSE, diag=TRUE, upper=TRUE)
enr_uwUF.dist = distance(enr.rare.physeq, method="unifrac")
enr_wUF.dist = distance(enr.rare.physeq, method="wunifrac")

PERMANOVA

print("Bray-Curtis dissimilarity")

## [1] "Bray-Curtis dissimilarity"

enr_BC.adonis = adonis(formula = enr_BC.dist ~ ecosystem * substrate * day, data = as(sample_data(enr.rare.physeq), "data.frame"))
enr_BC.adonis

## 
## Call:
## adonis(formula = enr_BC.dist ~ ecosystem * substrate * day, data = as(sample_data(enr.rare.physeq),      "data.frame")) 
## 
## Permutation: free
## Number of permutations: 999
## 
## Terms added sequentially (first to last)
## 
##                          Df SumsOfSqs MeanSqs F.Model      R2 Pr(>F)    
## ecosystem                 2    11.411  5.7053 28.3341 0.12269  0.001 ***
## substrate                 5     2.397  0.4794  2.3807 0.02577  0.001 ***
## day                       1     0.357  0.3571  1.7734 0.00384  0.062 .  
## ecosystem:substrate      10     1.156  0.1156  0.5741 0.01243  1.000    
## ecosystem:day             2     0.185  0.0923  0.4583 0.00198  0.999    
## substrate:day             5     0.303  0.0606  0.3010 0.00326  1.000    
## ecosystem:substrate:day  10     0.475  0.0475  0.2361 0.00511  1.000    
## Residuals               381    76.718  0.2014         0.82491           
## Total                   416    93.002                 1.00000           
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

print("-----")

## [1] "-----"

print("Unweighted UniFrac distance")

## [1] "Unweighted UniFrac distance"

enr_uwUF.adonis = adonis(formula = enr_uwUF.dist ~ ecosystem * substrate * day, data = as(sample_data(enr.rare.physeq), "data.frame"))
enr_uwUF.adonis

## 
## Call:
## adonis(formula = enr_uwUF.dist ~ ecosystem * substrate * day,      data = as(sample_data(enr.rare.physeq), "data.frame")) 
## 
## Permutation: free
## Number of permutations: 999
## 
## Terms added sequentially (first to last)
## 
##                          Df SumsOfSqs MeanSqs F.Model      R2 Pr(>F)    
## ecosystem                 2     5.890 2.94479 15.8179 0.07255  0.001 ***
## substrate                 5     1.071 0.21426  1.1509 0.01320  0.131    
## day                       1     0.276 0.27590  1.4820 0.00340  0.068 .  
## ecosystem:substrate      10     1.155 0.11548  0.6203 0.01422  1.000    
## ecosystem:day             2     0.241 0.12073  0.6485 0.00297  0.998    
## substrate:day             5     0.575 0.11499  0.6177 0.00708  1.000    
## ecosystem:substrate:day  10     1.046 0.10459  0.5618 0.01288  1.000    
## Residuals               381    70.930 0.18617         0.87370           
## Total                   416    81.184                 1.00000           
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

print("-----")

## [1] "-----"

print("Weighted UniFrac distance")

## [1] "Weighted UniFrac distance"

enr_wUF.adonis = adonis(formula = enr_wUF.dist ~ ecosystem * substrate * day, data = as(sample_data(enr.rare.physeq), "data.frame"))
enr_wUF.adonis

## 
## Call:
## adonis(formula = enr_wUF.dist ~ ecosystem * substrate * day,      data = as(sample_data(enr.rare.physeq), "data.frame")) 
## 
## Permutation: free
## Number of permutations: 999
## 
## Terms added sequentially (first to last)
## 
##                          Df SumsOfSqs MeanSqs F.Model      R2 Pr(>F)    
## ecosystem                 2    1.9896 0.99480  33.384 0.13575  0.001 ***
## substrate                 5    0.8335 0.16670   5.594 0.05687  0.001 ***
## day                       1    0.1283 0.12832   4.306 0.00876  0.002 ** 
## ecosystem:substrate      10    0.1916 0.01916   0.643 0.01307  0.992    
## ecosystem:day             2    0.0312 0.01559   0.523 0.00213  0.930    
## substrate:day             5    0.0649 0.01298   0.435 0.00443  1.000    
## ecosystem:substrate:day  10    0.0638 0.00638   0.214 0.00436  1.000    
## Residuals               381   11.3532 0.02980         0.77464           
## Total                   416   14.6561                 1.00000           
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

print("-----")

## [1] "-----"

Distances/Dissimilarities

Substrate significantly explains varaition in community composition as does ecosystem. Now I want to see if the differences between water controls and treatments correlate with either SOM or [DNA].

bulk_meta.df = data.frame(sample_data(bulk.rare.physeq)) %>%
  select(location, ecosystem, pH, organic_content_perc, DNA_conc__ng_ul) %>%
  mutate(DNA_conc__ng_ul = as.numeric(as.character(DNA_conc__ng_ul)))

H2O.metadata = data.frame(sample_data(enr.rare.physeq)) %>%
  mutate(X.Sample = as.character(X.Sample)) %>%
  filter(substrate == "H2O-Con") %>%
  rename(Control = X.Sample) %>%
  select(Control, day, ecosystem, location)

treat.metadata = data.frame(sample_data(enr.rare.physeq)) %>%
  mutate(X.Sample = as.character(X.Sample)) %>%
  filter(substrate != "H2O-Con") %>%
  rename(Treatment = X.Sample) %>%
  select(Treatment, substrate, day, ecosystem, location)

paired.metadata = inner_join(H2O.metadata, treat.metadata, by = c("day", "ecosystem", "location"))

enr_BC.dist.df = as.matrix(enr_BC.dist)[paired.metadata$Treatment, paired.metadata$Control] %>%
  as.data.frame %>%
  tibble::rownames_to_column(var="X.Sample") %>%
  rename(Treatment = X.Sample) %>%
  tidyr::gather(key=Control, value=bray, -Treatment)

enr_uwUF.dist.df = as.matrix(enr_uwUF.dist)[paired.metadata$Treatment, paired.metadata$Control] %>%
  as.data.frame %>%
  tibble::rownames_to_column(var="X.Sample") %>%
  rename(Treatment = X.Sample) %>%
  tidyr::gather(key=Control, value=uwUF, -Treatment)

enr_wUF.dist.df = as.matrix(enr_wUF.dist)[paired.metadata$Treatment, paired.metadata$Control] %>%
  as.data.frame %>%
  tibble::rownames_to_column(var="X.Sample") %>%
  rename(Treatment = X.Sample) %>%
  tidyr::gather(key=Control, value=wUF, -Treatment)

enr_dist.df = full_join(enr_BC.dist.df, enr_uwUF.dist.df, by = c("Treatment", "Control")) %>%
  full_join(enr_wUF.dist.df, by = c("Treatment", "Control")) %>%
  inner_join(paired.metadata, by = c("Treatment", "Control")) %>%
  mutate(time_point = ifelse(day %in% c(2, 14), "Early", "Late"),
         ecosystem = factor(ecosystem, levels = c("agriculture", "meadow", "forest")),
         substrate = factor(substrate, levels = c("12C-Xyl", "12C-Ami", "12C-Van", "12C-Cel", "12C-Pal"))) %>%
  left_join(bulk_meta.df, by = c("ecosystem", "location")) %>%
  mutate(location = gsub("_", " ", location))

Early timepoints

## Filter to just early timepoints
early_enr_dist.df = enr_dist.df %>%
  filter(time_point == "Early") %>%
  mutate(ecosystem = factor(ecosystem, levels=c("agriculture", "meadow", "forest")))

## Compare with organic content
early_SOM_wUF.df = data.frame()
for (sub in c("12C-Xyl", "12C-Ami", "12C-Van", "12C-Cel", "12C-Pal")){
  sub.wUF.df = filter(early_enr_dist.df, substrate==sub)
  cor.res = cor.test(x=sub.wUF.df$organic_content_perc, y=sub.wUF.df$wUF)
  model.df = data.frame(substrate = factor(sub, levels=c("12C-Xyl", "12C-Ami", "12C-Van", "12C-Cel", "12C-Pal")),
                        r = cor.res$estimate,
                        pvalue = cor.res$p.value)  %>%
    mutate(padj = p.adjust(pvalue, method = "BH", n = 5))
  early_SOM_wUF.df = rbind(early_SOM_wUF.df, model.df)
}

early_wUF_SOM.plot = ggplot(data=early_enr_dist.df, aes(x=organic_content_perc, y=wUF)) +
  geom_smooth(method="lm", se=FALSE, color="grey70", size=0.75) +
  geom_point(aes(color=ecosystem)) +
  geom_text(data=early_SOM_wUF.df, aes(label=paste("r==", round(r, 3), sep="")),
            x=0.04, y=0.4, hjust=0, parse = TRUE) +
  geom_text(data=early_SOM_wUF.df, aes(label=paste("p==", round(padj, 3), sep="")),
            x=0.04, y=0.36, hjust=0, parse = TRUE) +
  scale_color_manual(values = eco.col, labels = c("agriculture" = "Cropland", "meadow" = "Old-Field", "forest" = "Forest")) +
  lims(y=c(0, 0.45)) +
  labs(x="% SOM in bulk soil", y="Weighted UniFrac Distance", color="Land-use") +
  theme_bw() + 
  theme(axis.text.x = element_text(size=12, angle=90, vjust=0.5),
        axis.text.y = element_text(size=12),
        axis.title = element_text(size=12),
        strip.text = element_text(size=12),
        legend.text = element_text(size=12),
        legend.title = element_text(size=12),
        legend.position = "top") +
  facet_wrap(~factor(gsub("12C-", "", substrate), levels=c("Xyl", "Ami", "Van", "Cel", "Pal")), nrow=1)

## Compare with DNA concentration
early_DNA_wUF.df = data.frame()
for (sub in c("12C-Xyl", "12C-Ami", "12C-Van", "12C-Cel", "12C-Pal")){
  sub.wUF.df = filter(early_enr_dist.df, substrate==sub)
  cor.res = cor.test(x=sub.wUF.df$DNA_conc__ng_ul, y=sub.wUF.df$wUF)
  model.df = data.frame(substrate = factor(sub, levels=c("12C-Xyl", "12C-Ami", "12C-Van", "12C-Cel", "12C-Pal")),
                        r = cor.res$estimate,
                        pvalue = cor.res$p.value)  %>%
    mutate(padj = p.adjust(pvalue, method = "BH", n = 5))
  early_DNA_wUF.df = rbind(early_DNA_wUF.df, model.df)
}

early_wUF_DNA.plot = ggplot(data=early_enr_dist.df, aes(x=DNA_conc__ng_ul, y=wUF)) +
  geom_smooth(method="lm", se=FALSE, color="grey70", size=0.75) +
  geom_point(aes(color=ecosystem)) +
  geom_text(data=early_DNA_wUF.df, aes(label=paste("r==", round(r, 3), sep="")),
            x=5, y=0.4, hjust=0, parse = TRUE) +
  geom_text(data=early_DNA_wUF.df, aes(label=paste("p==", round(padj, 3), sep="")),
            x=5, y=0.36, hjust=0, parse = TRUE) +
  scale_color_manual(values = eco.col, labels = c("agriculture" = "Cropland", "meadow" = "Old-Field", "forest" = "Forest")) +
  lims(y=c(0, 0.45)) +
  labs(x="DNA Concentration", y="Weighted UniFrac Distance", color="Land-use") +
  theme_bw() + 
  theme(axis.text.x = element_text(size=12, angle=90, vjust=0.5),
        axis.text.y = element_text(size=12),
        axis.title = element_text(size=12),
        strip.text = element_text(size=12),
        legend.text = element_text(size=12),
        legend.title = element_text(size=12),
        legend.position = "top") +
  facet_wrap(~factor(gsub("12C-", "", substrate), levels=c("Xyl", "Ami", "Van", "Cel", "Pal")), nrow=1)

## Plot together

early_landuse.leg = g_legend(early_wUF_SOM.plot + theme(legend.direction = "horizontal", 
                                                        legend.position = "top",
                                                        legend.margin=unit(c(0,0,0,0),"cm")))

cowplot::plot_grid(early_landuse.leg,
                   early_wUF_SOM.plot + theme(legend.position = "none"),
                   early_wUF_DNA.plot + theme(legend.position = "none"), 
                   ncol=1, labels=c("", "A", "B"), rel_heights = c(0.1, 1, 1))

early_SOM_wUF.df

##      substrate          r      pvalue        padj
## cor    12C-Xyl -0.4394740 0.015102735 0.075513675
## cor1   12C-Ami -0.5635586 0.001183091 0.005915455
## cor2   12C-Van -0.5085936 0.004106756 0.020533781
## cor3   12C-Cel -0.3298842 0.075028616 0.375143082
## cor4   12C-Pal -0.3912330 0.035847250 0.179236250

early_DNA_wUF.df

##      substrate          r       pvalue        padj
## cor    12C-Xyl -0.3919137 0.0322006083 0.161003042
## cor1   12C-Ami -0.5861507 0.0006647827 0.003323913
## cor2   12C-Van -0.5794285 0.0007926151 0.003963076
## cor3   12C-Cel -0.1307706 0.4909543483 1.000000000
## cor4   12C-Pal -0.3894321 0.0367842529 0.183921265

Late timepoints

## Filter to just late timepoints
late_enr_dist.df = enr_dist.df %>%
  filter(time_point == "Late") %>%
  mutate(ecosystem = factor(ecosystem, levels=c("agriculture", "meadow", "forest")))

## Compare with organic content
late_SOM_wUF.df = data.frame()
for (sub in c("12C-Xyl", "12C-Ami", "12C-Van", "12C-Cel", "12C-Pal")){
  sub.wUF.df = filter(late_enr_dist.df, substrate==sub)
  cor.res = cor.test(x=sub.wUF.df$organic_content_perc, y=sub.wUF.df$wUF)
  model.df = data.frame(substrate = factor(sub, levels=c("12C-Xyl", "12C-Ami", "12C-Van", "12C-Cel", "12C-Pal")),
                        r = cor.res$estimate,
                        pvalue = cor.res$p.value)  %>%
    mutate(padj = p.adjust(pvalue, method = "BH", n = 5))
  late_SOM_wUF.df = rbind(late_SOM_wUF.df, model.df)
}

late_wUF_SOM.plot = ggplot(data=late_enr_dist.df, aes(x=organic_content_perc, y=wUF)) +
  geom_smooth(method="lm", se=FALSE, color="grey70", size=0.75) +
  geom_point(aes(color=ecosystem)) +
  geom_text(data=late_SOM_wUF.df, aes(label=paste("r==", round(r, 3), sep="")),
            x=0.04, y=0.4, hjust=0, parse = TRUE) +
  geom_text(data=late_SOM_wUF.df, aes(label=paste("p==", round(padj, 3), sep="")),
            x=0.04, y=0.36, hjust=0, parse = TRUE) +
  scale_color_manual(values = eco.col, labels = c("agriculture" = "Cropland", "meadow" = "Old-Field", "forest" = "Forest")) +
  lims(y=c(0, 0.45)) +
  labs(x="% SOM in bulk soil", y="Weighted UniFrac Distance", color="Land-use") +
  theme_bw() + 
  theme(axis.text.x = element_text(size=12, angle=90, vjust=0.5),
        axis.text.y = element_text(size=12),
        axis.title = element_text(size=12),
        strip.text = element_text(size=12),
        legend.text = element_text(size=12),
        legend.title = element_text(size=12),
        legend.position = "top") +
  facet_wrap(~factor(gsub("12C-", "", substrate), levels=c("Xyl", "Ami", "Van", "Cel", "Pal")), nrow=1)

## Compare with DNA concentration
late_DNA_wUF.df = data.frame()
for (sub in c("12C-Xyl", "12C-Ami", "12C-Van", "12C-Cel", "12C-Pal")){
  sub.wUF.df = filter(late_enr_dist.df, substrate==sub)
  cor.res = cor.test(x=sub.wUF.df$DNA_conc__ng_ul, y=sub.wUF.df$wUF)
  model.df = data.frame(substrate = factor(sub, levels=c("12C-Xyl", "12C-Ami", "12C-Van", "12C-Cel", "12C-Pal")),
                        r = cor.res$estimate,
                        pvalue = cor.res$p.value)  %>%
    mutate(padj = p.adjust(pvalue, method = "BH", n = 5))
  late_DNA_wUF.df = rbind(late_DNA_wUF.df, model.df)
}

late_wUF_DNA.plot = ggplot(data=late_enr_dist.df, aes(x=DNA_conc__ng_ul, y=wUF)) +
  geom_smooth(method="lm", se=FALSE, color="grey70", size=0.75) +
  geom_point(aes(color=ecosystem)) +
  geom_text(data=late_DNA_wUF.df, aes(label=paste("r==", round(r, 3), sep="")),
            x=5, y=0.4, hjust=0, parse = TRUE) +
  geom_text(data=late_DNA_wUF.df, aes(label=paste("p==", round(padj, 3), sep="")),
            x=5, y=0.36, hjust=0, parse = TRUE) +
  scale_color_manual(values = eco.col, labels = c("agriculture" = "Cropland", "meadow" = "Old-Field", "forest" = "Forest")) +
  lims(y=c(0, 0.45)) +
  labs(x="DNA Concentration", y="Weighted UniFrac Distance", color="Land-use") +
  theme_bw() + 
  theme(axis.text.x = element_text(size=12, angle=90, vjust=0.5),
        axis.text.y = element_text(size=12),
        axis.title = element_text(size=12),
        strip.text = element_text(size=12),
        legend.text = element_text(size=12),
        legend.title = element_text(size=12),
        legend.position = "top") +
  facet_wrap(~factor(gsub("12C-", "", substrate), levels=c("Xyl", "Ami", "Van", "Cel", "Pal")), nrow=1)

## Plot together

late_landuse.leg = g_legend(late_wUF_SOM.plot + theme(legend.direction = "horizontal", 
                                            legend.position = "top",
                                            legend.margin=unit(c(0,0,0,0),"cm")))

cowplot::plot_grid(late_landuse.leg,
                   late_wUF_SOM.plot + theme(legend.position = "none"),
                   late_wUF_DNA.plot + theme(legend.position = "none"), 
                   ncol=1, labels=c("", "A", "B"), rel_heights = c(0.1, 1, 1))

late_SOM_wUF.df

##      substrate          r       pvalue        padj
## cor    12C-Xyl -0.5665699 0.0010981916 0.005490958
## cor1   12C-Ami -0.4295573 0.0178374274 0.089187137
## cor2   12C-Van -0.6256795 0.0002176509 0.001088255
## cor3   12C-Cel -0.4483317 0.0147202467 0.073601234
## cor4   12C-Pal -0.4150801 0.0251522068 0.125761034

late_DNA_wUF.df

##      substrate          r       pvalue        padj
## cor    12C-Xyl -0.5763353 0.0008583402 0.004291701
## cor1   12C-Ami -0.2342091 0.2128667039 1.000000000
## cor2   12C-Van -0.5619261 0.0012314691 0.006157345
## cor3   12C-Cel -0.4051031 0.0292561374 0.146280687
## cor4   12C-Pal -0.2615567 0.1705101106 0.852550553

Diversity plots for publication

These will be the plots from the analyses above that are used in the publication of this study (and my PhD dissertation).

Early timepoints

early_landuse.leg = g_legend(early_evenness_SOM.plot + theme(legend.direction = "vertical", 
                                                             legend.box.background = element_rect(colour = "black")))

early_DNAyield.plot = cowplot::plot_grid(early_evenness_wilcox.plot + theme(legend.position = "none"),
                                         early_evenness_DNA.plot + theme(legend.position = "none") + labs(x="DNA yield from bulk soil (ng/µl)", y="Change in evenness"),
                                         early_landuse.leg,
                                         early_wUF_DNA.plot + theme(legend.position = "none") + labs(x="DNA yield from bulk soil (ng/µl)", y="Weighted UniFrac distance"), 
                                         ncol=2, labels=c("A", "B", "", "C"), rel_widths = c(0.5, 1, 0.5, 1))

early_DNAyield.plot

#ggsave(early_DNAyield.plot, filename = "/Users/sambarnett/Documents/Dissertation/figures/fig2_5.tiff", 
#       device = "tiff", width = 7, height = 6, units = "in")

For publication

carbon.conv = data.frame(substrate = c("12C-Xyl", "12C-Ami", "12C-Van", "12C-Cel", "12C-Pal"),
                         carbon = factor(c("Xylose", "Amino acids", "Vanillin", "Cellulose", "Palmitic acid"),
                                         levels=c("Xylose", "Amino acids", "Vanillin", "Cellulose", "Palmitic acid")))

pub_early_evenness_wilcox.plot = ggplot(data=left_join(evenness_early_meta.df, carbon.conv, by="substrate"), aes(x=carbon, y=delta_evenness)) +
  geom_hline(yintercept = 0, linetype=2, color="red") +
  geom_boxplot(outlier.shape = NA) +
  geom_jitter(aes(color=ecosystem), alpha=0.5, size=1.5) +
  geom_text(data=filter(left_join(early_evenness_wilcox.df, carbon.conv, by="substrate"), padj < 0.05), aes(x=carbon, label=sig), y=0.06, size=4) +
  scale_color_manual(values = eco.col, labels = c("agriculture" = "Cropland", "meadow" = "Old-Field", "forest" = "Forest")) +
  labs(x="Substrate", y="Change in evenness", color="Land-use") +
  lims(y=c(-0.3, 0.1)) +
  theme_bw() + 
  theme(axis.text.x = element_text(size=6, angle=45, vjust=1, hjust=1),
        axis.text.y = element_text(size=6),
        axis.title = element_text(size=7),
        axis.ticks = element_line(size=0.2),
        strip.text = element_text(size=6),
        legend.text = element_text(size=6),
        legend.title = element_text(size=7),
        legend.position = "right",
        legend.box.background = element_rect(colour = "black"))

pub_early_evenness_DNA.plot = ggplot(data=left_join(evenness_early_meta.df, carbon.conv, by="substrate"), aes(x=DNA_conc__ng_ul, y=delta_evenness)) +
  geom_smooth(method="lm", se=FALSE, color="black", size=0.75) +
  geom_point(aes(color=ecosystem), size=1.5) +
  geom_text(data=left_join(early_DNA_evenness.df, carbon.conv, by="substrate"), aes(label=paste("r==", round(r, 3), sep="")),
            x=15, y=-0.2, hjust=0, parse = TRUE, size=(6*5/14)) +
  geom_text(data=left_join(early_DNA_evenness.df, carbon.conv, by="substrate"), aes(label=paste("p==", round(padj, 3), sep="")),
            x=15, y=-0.235, hjust=0, parse = TRUE, size=(6*5/14)) +
  scale_color_manual(values = eco.col, labels = c("agriculture" = "Cropland", "meadow" = "Old-Field", "forest" = "Forest")) +
  scale_x_continuous(breaks=c(0,  50, 100)) +
  lims(y=c(-0.3, 0.1)) +
  labs(x="DNA Concentration", y="Change in evenness", color="Land-use") +
  theme_bw() + 
  theme(axis.text.x = element_text(size=6, angle=90, vjust=0.5),
        axis.text.y = element_text(size=6),
        axis.title = element_text(size=7),
        axis.ticks = element_line(size=0.2),
        strip.text = element_text(size=6),
        legend.text = element_text(size=6),
        legend.title = element_text(size=7),
        legend.position = "right") +
  facet_wrap(~carbon, nrow=1)


pub_early_wUF_DNA.plot = ggplot(data=left_join(early_enr_dist.df, carbon.conv, by="substrate"), aes(x=DNA_conc__ng_ul, y=wUF)) +
  geom_smooth(method="lm", se=FALSE, color="black", size=0.75) +
  geom_point(aes(color=ecosystem), size=1.5) +
  geom_text(data=left_join(early_DNA_wUF.df, carbon.conv, by="substrate"), aes(label=paste("r==", round(r, 3), sep="")),
            x=15, y=0.4, hjust=0, parse = TRUE, size=(6*5/14)) +
  geom_text(data=left_join(early_DNA_wUF.df, carbon.conv, by="substrate"), aes(label=paste("p==", round(padj, 3), sep="")),
            x=15, y=0.36, hjust=0, parse = TRUE, size=(6*5/14)) +
  scale_color_manual(values = eco.col, labels = c("agriculture" = "Cropland", "meadow" = "Old-Field", "forest" = "Forest")) +
  scale_x_continuous(breaks=c(0,  50, 100)) +
  lims(y=c(0, 0.45)) +
  labs(x="DNA Concentration", y="Weighted UniFrac Distance", color="Land-use") +
  theme_bw() + 
  theme(axis.text.x = element_text(size=6, angle=90, vjust=0.5),
        axis.text.y = element_text(size=6),
        axis.title = element_text(size=7),
        axis.ticks = element_line(size=0.2),
        strip.text = element_text(size=6),
        legend.text = element_text(size=6),
        legend.title = element_text(size=7),
        legend.position = "right") +
  facet_wrap(~carbon, nrow=1)

pub_early_landuse.leg = g_legend(pub_early_wUF_DNA.plot + theme(legend.box.background = element_rect(colour = "black"),
                                                                legend.title = element_text(size=7, hjust=0.5)))


pub_early_evenness_wilcox_leg.plot = cowplot::plot_grid(pub_early_evenness_wilcox.plot + theme(legend.position = "none"), 
                                                        pub_early_landuse.leg, ncol=2, rel_widths = c(1, 0.41))

pub_early_DNAyield.plot = cowplot::plot_grid(pub_early_evenness_wilcox_leg.plot,
                                             pub_early_evenness_DNA.plot + theme(legend.position = "none") + labs(x="DNA yield from bulk soil (ng/µl)", y="Change in evenness"),
                                             pub_early_wUF_DNA.plot + theme(legend.position = "none") + labs(x="DNA yield from bulk soil (ng/µl)", y="Weighted UniFrac distance"), 
                                             ncol=1, labels=c("a", "b", "c"), label_size = 10, rel_heights = c(0.9, 1, 1))

pub_early_DNAyield.plot

#ggsave(pub_early_DNAyield.plot, filename = "/Users/sambarnett/Documents/Buckley Lab/FullCyc2/manuscript/Figures/Fig3.tiff", 
#       device = "tiff", width = 3.46457, height = 7.08661, units = "in")

early_landuse.leg = g_legend(early_wUF_SOM.plot + theme(legend.direction = "horizontal", 
                                                        legend.position = "top",
                                                        legend.margin=unit(c(0,0,0,0),"cm")))
early_SOM.plot = cowplot::plot_grid(early_landuse.leg,
                                    early_evenness_SOM.plot + theme(legend.position = "none") + labs(x="Percent SOM in bulk soil (%)", y="Change in evenness"),
                                    early_wUF_SOM.plot + theme(legend.position = "none") + labs(x="Percent SOM in bulk soil (%)", y="Weighted UniFrac distance"), 
                                    ncol=1, labels=c("", "A", "B"), rel_heights = c(0.1, 1, 1))

early_SOM.plot

#ggsave(early_SOM.plot, filename = "/Users/sambarnett/Documents/Dissertation/figures/figS2_11.tiff", 
#       device = "tiff", width = 5, height = 7, units = "in")

Late timepoints

late_landuse.leg = g_legend(late_evenness_SOM.plot + theme(legend.direction = "vertical", 
                                                           legend.box.background = element_rect(colour = "black")))

late_DNAyield.plot = cowplot::plot_grid(late_evenness_wilcox.plot + theme(legend.position = "none"),
                                        late_evenness_DNA.plot + theme(legend.position = "none") + labs(x="DNA yield from bulk soil (ng/µl)", y="Change in evenness"),
                                        late_landuse.leg,
                                        late_wUF_DNA.plot + theme(legend.position = "none") + labs(x="DNA yield from bulk soil (ng/µl)", y="Weighted UniFrac distance"), 
                                        ncol=2, labels=c("A", "B", "", "C"), rel_widths = c(0.5, 1, 0.5, 1))

late_DNAyield.plot

#ggsave(late_DNAyield.plot, filename = "/Users/sambarnett/Documents/Dissertation/figures/figS2_12.tiff", 
#       device = "tiff", width = 7, height = 6, units = "in")

late_landuse.leg = g_legend(late_wUF_SOM.plot + theme(legend.direction = "horizontal", 
                                                      legend.position = "top",
                                                      legend.margin=unit(c(0,0,0,0),"cm")))
late_SOM.plot = cowplot::plot_grid(late_landuse.leg,
                                   late_evenness_SOM.plot + theme(legend.position = "none") + labs(x="Percent SOM in bulk soil (%)", y="Change in evenness"),
                                   late_wUF_SOM.plot + theme(legend.position = "none") + labs(x="Percent SOM in bulk soil (%)", y="Weighted UniFrac distance"), 
                                   ncol=1, labels=c("", "A", "B"), rel_heights = c(0.1, 1, 1))

late_SOM.plot

#ggsave(late_SOM.plot, filename = "/Users/sambarnett/Documents/Dissertation/figures/figS2_13.tiff", 
#       device = "tiff", width = 5, height = 7, units = "in")

DESeq2 significant enrichments

Now I want to see if I can identify any OTUs that are significantly enriched in the substrate treated samples compared to the control samples.

library(DESeq2)
library(knitr)

enr.deseq.df = data.frame()
for (eco in c("agriculture", "meadow", "forest")){
  for (carbon in c("12C-Xyl", "12C-Ami", "12C-Van", "12C-Cel", "12C-Pal")){
    for (dia in unique(filter(data.frame(sample_data(enr.physeq)), ecosystem == eco, substrate == carbon)$day)){
      sub.physeq = subset_samples(enr.physeq, ecosystem == eco & substrate %in% c(carbon, "H2O-Con") & day == dia)
      sub.physeq = prune_taxa(taxa_sums(sub.physeq) > 0, sub.physeq)
      sample_data(sub.physeq)$substrate = as.factor(sample_data(sub.physeq)$substrate)
      sample_data(sub.physeq)$substrate = relevel(sample_data(sub.physeq)$substrate, "H2O-Con")
      
      OTU.table = as.matrix(otu_table(sub.physeq))
      OTU.table[OTU.table < 5] = 0
      OTU.table[OTU.table >= 5] = 1
      sparse = length(sample_names(sub.physeq)) * 0.25
      sampleCount.df = data.frame(sample = rowSums(OTU.table)) %>%
        tibble::rownames_to_column(var="OTU") %>%
        filter(sample >= sparse)
      sub.physeq = prune_taxa(sampleCount.df$OTU, sub.physeq)
      sub.deseq = phyloseq_to_deseq2(sub.physeq, ~ location + substrate)
      sub.deseq = DESeq(sub.deseq, betaPrior=TRUE)
      sub.deseq.res = results(sub.deseq, lfcThreshold = .25,
                              contrast = c("substrate", carbon, "H2O-Con"), 
                              altHypothesis = "greater",
                              test="Wald")
      enr.deseq.df = rbind(enr.deseq.df, data.frame(sub.deseq.res) %>%
                             tibble::rownames_to_column(var="OTU") %>%
                             mutate(substrate = carbon, ecosystem = eco, day = dia))
    }
  }
}

#write.table(enr.deseq.df, file = "/Users/sambarnett/Documents/Buckley Lab/FullCyc2/enrichment_DESeq2_l2fc.txt", sep="\t", quote=FALSE, row.names = FALSE)

kable(enr.deseq.df %>% 
        filter(padj < 0.05) %>%
        group_by(substrate, ecosystem, day) %>%
        mutate(n_total_OTU = n()))

OTU	baseMean	log2FoldChange	lfcSE	stat	pvalue	padj	substrate	ecosystem	day	n_total_OTU
OTU.463	57.143056	1.7055406	0.3470587	4.193932	0.0000137	0.0118094	12C-Xyl	agriculture	4	3
OTU.66	108.855465	1.8827157	0.2496612	6.539725	0.0000000	0.0000001	12C-Xyl	agriculture	4	3
OTU.2	215.880615	1.1472726	0.2351432	3.815856	0.0000679	0.0389719	12C-Xyl	agriculture	4	3
OTU.9	823.875478	1.4954986	0.1899400	6.557326	0.0000000	0.0000000	12C-Xyl	agriculture	2	10
OTU.463	176.220818	2.3651105	0.3061382	6.909005	0.0000000	0.0000000	12C-Xyl	agriculture	2	10
OTU.1541	63.550455	1.1522105	0.2627946	3.433139	0.0002983	0.0098445	12C-Xyl	agriculture	2	10
OTU.951	84.136147	0.8679262	0.2059635	3.000173	0.0013491	0.0400692	12C-Xyl	agriculture	2	10
OTU.154	83.888332	1.5065962	0.2482874	5.061054	0.0000002	0.0000155	12C-Xyl	agriculture	2	10
OTU.315	477.770963	1.5345565	0.3461871	3.710585	0.0001034	0.0051178	12C-Xyl	agriculture	2	10
OTU.8	1616.901682	1.2408664	0.2885439	3.434023	0.0002973	0.0098445	12C-Xyl	agriculture	2	10
OTU.66	124.839100	1.9549236	0.3147849	5.416154	0.0000000	0.0000030	12C-Xyl	agriculture	2	10
OTU.2	249.987495	1.3658821	0.3072398	3.631958	0.0001406	0.0059671	12C-Xyl	agriculture	2	10
OTU.187	64.905291	1.6165815	0.2975099	4.593398	0.0000022	0.0001295	12C-Xyl	agriculture	2	10
OTU.9	1402.221020	2.2974544	0.2917992	7.016655	0.0000000	0.0000000	12C-Ami	agriculture	4	14
OTU.463	148.253559	2.4180415	0.3429677	6.321416	0.0000000	0.0000000	12C-Ami	agriculture	4	14
OTU.3782	12.433210	1.9041180	0.3626178	4.561601	0.0000025	0.0002191	12C-Ami	agriculture	4	14
OTU.315	392.139985	1.9600863	0.2492455	6.861052	0.0000000	0.0000000	12C-Ami	agriculture	4	14
OTU.8	1100.658842	1.6892232	0.2190543	6.570168	0.0000000	0.0000000	12C-Ami	agriculture	4	14
OTU.2396	27.086295	1.8923076	0.3811749	4.308541	0.0000082	0.0006384	12C-Ami	agriculture	4	14
OTU.23	93.608292	1.5439977	0.2326064	5.563036	0.0000000	0.0000015	12C-Ami	agriculture	4	14
OTU.1386	58.258922	1.2905180	0.2966817	3.507186	0.0002264	0.0125672	12C-Ami	agriculture	4	14
OTU.4461	24.743522	1.5908981	0.3615954	3.708283	0.0001043	0.0067557	12C-Ami	agriculture	4	14
OTU.7776	115.221961	0.8563077	0.1714353	3.536657	0.0002026	0.0121100	12C-Ami	agriculture	4	14
OTU.66	179.139721	2.6579874	0.2494062	9.654884	0.0000000	0.0000000	12C-Ami	agriculture	4	14
OTU.2	460.160815	1.6191671	0.3311080	4.135108	0.0000177	0.0012530	12C-Ami	agriculture	4	14
OTU.16149	21.593242	3.4617346	0.4060604	7.909500	0.0000000	0.0000000	12C-Ami	agriculture	4	14
OTU.3	31.418578	2.3544258	0.4180531	5.033872	0.0000002	0.0000233	12C-Ami	agriculture	4	14
OTU.3589	14.800817	1.5392306	0.4049181	3.183929	0.0007265	0.0303748	12C-Ami	agriculture	2	17
OTU.9	2031.842266	2.6071517	0.2382931	9.891816	0.0000000	0.0000000	12C-Ami	agriculture	2	17
OTU.463	245.424035	2.6656187	0.3248678	7.435699	0.0000000	0.0000000	12C-Ami	agriculture	2	17
OTU.3782	14.552128	2.0661450	0.3948885	4.599134	0.0000021	0.0001774	12C-Ami	agriculture	2	17
OTU.315	535.845657	2.2230735	0.2927950	6.738753	0.0000000	0.0000000	12C-Ami	agriculture	2	17
OTU.8	1912.137734	1.8269501	0.2647118	5.957233	0.0000000	0.0000001	12C-Ami	agriculture	2	17
OTU.2396	43.956143	1.8111898	0.4107461	3.800863	0.0000721	0.0043848	12C-Ami	agriculture	2	17
OTU.2513	62.213661	1.1788555	0.2616336	3.550216	0.0001925	0.0091967	12C-Ami	agriculture	2	17
OTU.1386	66.543042	2.1527448	0.2855800	6.662739	0.0000000	0.0000000	12C-Ami	agriculture	2	17
OTU.4461	32.142982	1.6209049	0.3337316	4.107807	0.0000200	0.0013361	12C-Ami	agriculture	2	17
OTU.193	72.464175	2.3130630	0.4535396	4.548804	0.0000027	0.0002005	12C-Ami	agriculture	2	17
OTU.20	757.867525	0.7834658	0.1667190	3.199790	0.0006876	0.0303748	12C-Ami	agriculture	2	17
OTU.66	210.384740	2.3425108	0.3797888	5.509670	0.0000000	0.0000017	12C-Ami	agriculture	2	17
OTU.2	546.506627	1.6335341	0.3685922	3.753563	0.0000872	0.0048597	12C-Ami	agriculture	2	17
OTU.3	113.241648	2.8100466	0.4301395	5.951666	0.0000000	0.0000001	12C-Ami	agriculture	2	17
OTU.711	32.047650	1.3359246	0.3043876	3.567572	0.0001802	0.0091967	12C-Ami	agriculture	2	17
OTU.187	53.188007	1.0761760	0.2623858	3.148707	0.0008200	0.0322683	12C-Ami	agriculture	2	17
OTU.1	980.734255	2.6471368	0.5128590	4.674066	0.0000015	0.0013451	12C-Van	agriculture	2	4
OTU.66	211.157488	2.0560193	0.4797711	3.764335	0.0000835	0.0380326	12C-Van	agriculture	2	4
OTU.2	2180.028131	2.1732379	0.4791039	4.014240	0.0000298	0.0181100	12C-Van	agriculture	2	4
OTU.3	1596.345820	3.3653851	0.4892176	6.368097	0.0000000	0.0000002	12C-Van	agriculture	2	4
OTU.8	1850.903401	2.3176915	0.3966413	5.213001	0.0000001	0.0000388	12C-Van	agriculture	4	6
OTU.2396	90.659803	3.0985649	0.4914833	5.795852	0.0000000	0.0000028	12C-Van	agriculture	4	6
OTU.1	589.750952	3.0252483	0.5020376	5.527970	0.0000000	0.0000090	12C-Van	agriculture	4	6
OTU.66	167.914591	2.7058574	0.3884788	6.321728	0.0000000	0.0000002	12C-Van	agriculture	4	6
OTU.2	1069.948292	2.3885562	0.4823726	4.433412	0.0000046	0.0012908	12C-Van	agriculture	4	6
OTU.56	193.226833	2.0932142	0.3779803	4.876482	0.0000005	0.0001804	12C-Van	agriculture	4	6
OTU.18	83.450918	1.4174655	0.2508977	4.653154	0.0000016	0.0032068	12C-Cel	agriculture	14	1
OTU.131	43.772174	1.5132675	0.3099683	4.075473	0.0000230	0.0165366	12C-Pal	agriculture	28	3
OTU.18	42.941360	1.4081744	0.2860221	4.049247	0.0000257	0.0165366	12C-Pal	agriculture	28	3
OTU.4420	113.862921	2.3114293	0.3187109	6.468023	0.0000000	0.0000001	12C-Pal	agriculture	28	3
OTU.9	307.578454	1.5465536	0.2465028	5.259791	0.0000001	0.0000171	12C-Xyl	meadow	2	7
OTU.951	23.722085	1.4350722	0.2565998	4.618367	0.0000019	0.0003055	12C-Xyl	meadow	2	7
OTU.154	110.673703	1.4643399	0.2625814	4.624622	0.0000019	0.0003055	12C-Xyl	meadow	2	7
OTU.2396	19.764453	1.6472424	0.3547402	3.938777	0.0000409	0.0055457	12C-Xyl	meadow	2	7
OTU.66	28.053223	2.0078524	0.2912671	6.035190	0.0000000	0.0000003	12C-Xyl	meadow	2	7
OTU.2	180.311952	1.7821720	0.2239602	6.841270	0.0000000	0.0000000	12C-Xyl	meadow	2	7
OTU.187	51.968729	1.6904341	0.2269908	6.345781	0.0000000	0.0000001	12C-Xyl	meadow	2	7
OTU.2	291.607731	1.5134239	0.2343700	5.390724	0.0000000	0.0000269	12C-Ami	meadow	4	2
OTU.3	67.375845	2.3313231	0.3385215	6.148274	0.0000000	0.0000006	12C-Ami	meadow	4	2
OTU.9	390.716931	1.9120133	0.2430766	6.837407	0.0000000	0.0000000	12C-Ami	meadow	2	8
OTU.3782	6.986803	1.5342235	0.3496703	3.672670	0.0001200	0.0213026	12C-Ami	meadow	2	8
OTU.1863	63.905406	1.7674692	0.3457300	4.389175	0.0000057	0.0013464	12C-Ami	meadow	2	8
OTU.315	168.680186	1.4360614	0.2688758	4.411186	0.0000051	0.0013464	12C-Ami	meadow	2	8
OTU.66	27.246631	1.6959146	0.3149271	4.591267	0.0000022	0.0007820	12C-Ami	meadow	2	8
OTU.2	256.841475	1.8312129	0.2188198	7.226096	0.0000000	0.0000000	12C-Ami	meadow	2	8
OTU.16149	9.175487	1.7383960	0.3551714	4.190641	0.0000139	0.0028214	12C-Ami	meadow	2	8
OTU.3	41.985175	2.6785968	0.3498060	6.942696	0.0000000	0.0000000	12C-Ami	meadow	2	8
OTU.3782	18.765526	2.3314754	0.3499449	5.948010	0.0000000	0.0000010	12C-Van	meadow	4	5
OTU.66	45.834871	1.7014041	0.3599458	4.032285	0.0000276	0.0104398	12C-Van	meadow	4	5
OTU.2	828.031945	2.7981725	0.2536109	10.047569	0.0000000	0.0000000	12C-Van	meadow	4	5
OTU.3	367.006897	1.8751572	0.3784055	4.294750	0.0000087	0.0044072	12C-Van	meadow	4	5
OTU.29	23.315455	1.6340944	0.3734184	3.706551	0.0001051	0.0317673	12C-Van	meadow	4	5
OTU.85	50.025534	1.4205890	0.3140419	3.727494	0.0000967	0.0070973	12C-Van	meadow	2	12
OTU.35	83.736607	1.1173289	0.2567176	3.378533	0.0003644	0.0202832	12C-Van	meadow	2	12
OTU.2977	43.992439	1.4438190	0.3223333	3.703679	0.0001062	0.0070973	12C-Van	meadow	2	12
OTU.98	26.822922	1.5949465	0.3292538	4.084832	0.0000221	0.0019933	12C-Van	meadow	2	12
OTU.3782	20.076912	3.0564123	0.3232447	8.682006	0.0000000	0.0000000	12C-Van	meadow	2	12
OTU.1166	16.420254	1.6638561	0.2746476	5.147891	0.0000001	0.0000147	12C-Van	meadow	2	12
OTU.2396	50.033273	2.6333978	0.3383678	7.043808	0.0000000	0.0000000	12C-Van	meadow	2	12
OTU.66	46.853527	2.5619807	0.3194617	7.237114	0.0000000	0.0000000	12C-Van	meadow	2	12
OTU.2	818.088037	2.2775018	0.3727713	5.438996	0.0000000	0.0000036	12C-Van	meadow	2	12
OTU.16149	12.255583	1.6074319	0.4011831	3.383572	0.0003577	0.0202832	12C-Van	meadow	2	12
OTU.3	598.496711	3.2789701	0.4011677	7.550383	0.0000000	0.0000000	12C-Van	meadow	2	12
OTU.223	42.460611	1.9160213	0.4097038	4.066405	0.0000239	0.0019933	12C-Van	meadow	2	12
OTU.1	354.827089	0.9391310	0.1456613	4.731050	0.0000011	0.0016920	12C-Xyl	forest	4	2
OTU.2	2283.299024	1.2753339	0.2374383	4.318317	0.0000079	0.0059549	12C-Xyl	forest	4	2
OTU.9	279.245634	1.2881788	0.1957360	5.303976	0.0000001	0.0000441	12C-Ami	forest	2	2
OTU.3	161.998275	2.7876475	0.3552902	7.142463	0.0000000	0.0000000	12C-Ami	forest	2	2
OTU.2	2911.058579	1.0187588	0.1898262	4.049804	0.0000256	0.0408034	12C-Ami	forest	4	1
OTU.66	64.385275	1.5741686	0.2865746	4.620677	0.0000019	0.0026750	12C-Van	forest	2	2
OTU.2	3957.520597	1.7175488	0.3265153	4.494579	0.0000035	0.0026750	12C-Van	forest	2	2
OTU.2	4835.350981	1.4233702	0.2440426	4.808054	0.0000008	0.0012177	12C-Van	forest	4	1

Taxonomy of enriched OTUs

enr.deseq.tax.df = data.frame(tax_table(enr.physeq), stringsAsFactors = FALSE) %>%
  tibble::rownames_to_column(var="OTU") %>%
  right_join(enr.deseq.df, by="OTU")

enr.deseq.tax.sum = enr.deseq.tax.df %>%
  filter(padj < 0.05) %>%
  mutate(taxa = ifelse(Phylum == "Proteobacteria", Class, 
                       ifelse(is.na(Phylum), "Unclassified", Phylum)),
         period = ifelse(day %in% c(2, 14), "Early", "Late")) %>%
  group_by(ecosystem, substrate, period, taxa) %>%
  summarize(n_OTU = n()) %>%
  as.data.frame

ggplot(data = enr.deseq.tax.sum, aes(x=ecosystem, y=n_OTU, fill=taxa)) +
  geom_bar(stat = "identity") +
  facet_grid(substrate~period)

OTUs confirming SIP results

Are any of these substrate enriched OTUs also 13C-labeled in the SIP study?

## Save results for publication table
enr.tax.df = data.frame(tax_table(enr.physeq), stringsAsFactors = FALSE) %>%
  tibble::rownames_to_column(var="OTU") %>%
  filter(OTU %in% filter(enr.deseq.df, padj < 0.05)$OTU)

incorp.enr.df = l2fc.df %>%
  filter(padj < 0.05) %>%
  select(OTU, substrate, ecosystem) %>%
  unique %>%
  mutate(substrate = gsub("13C", "12C", substrate),
         SIP_labeled = "Yes") %>%
  inner_join(enr.deseq.df %>% filter(padj < 0.05), 
             by = c("OTU", "substrate", "ecosystem")) %>%
  select(OTU, substrate, ecosystem, SIP_labeled) %>%
  right_join(enr.deseq.df %>% filter(padj < 0.05),
             by = c("OTU", "substrate", "ecosystem")) %>%
  mutate(SIP_labeled = ifelse(is.na(SIP_labeled), "No", SIP_labeled)) %>%
  left_join(enr.tax.df, by="OTU")

#write.table(incorp.enr.df, file = "/Users/sambarnett/Desktop/labeled_enrichment_DESeq2_l2fc.txt", sep="\t", quote=FALSE, row.names = FALSE)

kable(incorp.enr.df)

OTU	substrate	ecosystem	SIP_labeled	baseMean	log2FoldChange	lfcSE	stat	pvalue	padj	day	Domain	Phylum	Class	Order	Family	Genus	Species
OTU.66	12C-Ami	meadow	Yes	27.246631	1.6959146	0.3149271	4.591267	0.0000022	0.0007820	2	Bacteria	Proteobacteria	Gammaproteobacteria	Betaproteobacteriales	Burkholderiaceae	Burkholderia-Caballeronia-Paraburkholderia	NA
OTU.9	12C-Ami	meadow	Yes	390.716931	1.9120133	0.2430766	6.837407	0.0000000	0.0000000	2	Bacteria	Actinobacteria	Actinobacteria	Micrococcales	Micrococcaceae	Ambiguous_taxa	Ambiguous_taxa
OTU.3	12C-Ami	meadow	Yes	67.375845	2.3313231	0.3385215	6.148274	0.0000000	0.0000006	4	Bacteria	Proteobacteria	Gammaproteobacteria	Pseudomonadales	Pseudomonadaceae	Pseudomonas	Ambiguous_taxa
OTU.3	12C-Ami	meadow	Yes	41.985175	2.6785968	0.3498060	6.942696	0.0000000	0.0000000	2	Bacteria	Proteobacteria	Gammaproteobacteria	Pseudomonadales	Pseudomonadaceae	Pseudomonas	Ambiguous_taxa
OTU.3	12C-Ami	meadow	Yes	67.375845	2.3313231	0.3385215	6.148274	0.0000000	0.0000006	4	Bacteria	Proteobacteria	Gammaproteobacteria	Pseudomonadales	Pseudomonadaceae	Pseudomonas	Ambiguous_taxa
OTU.3	12C-Ami	meadow	Yes	41.985175	2.6785968	0.3498060	6.942696	0.0000000	0.0000000	2	Bacteria	Proteobacteria	Gammaproteobacteria	Pseudomonadales	Pseudomonadaceae	Pseudomonas	Ambiguous_taxa
OTU.2	12C-Ami	meadow	Yes	291.607731	1.5134239	0.2343700	5.390724	0.0000000	0.0000269	4	Bacteria	Proteobacteria	Gammaproteobacteria	Betaproteobacteriales	Burkholderiaceae	Burkholderia-Caballeronia-Paraburkholderia	NA
OTU.2	12C-Ami	meadow	Yes	256.841475	1.8312129	0.2188198	7.226096	0.0000000	0.0000000	2	Bacteria	Proteobacteria	Gammaproteobacteria	Betaproteobacteriales	Burkholderiaceae	Burkholderia-Caballeronia-Paraburkholderia	NA
OTU.2	12C-Ami	meadow	Yes	291.607731	1.5134239	0.2343700	5.390724	0.0000000	0.0000269	4	Bacteria	Proteobacteria	Gammaproteobacteria	Betaproteobacteriales	Burkholderiaceae	Burkholderia-Caballeronia-Paraburkholderia	NA
OTU.2	12C-Ami	meadow	Yes	256.841475	1.8312129	0.2188198	7.226096	0.0000000	0.0000000	2	Bacteria	Proteobacteria	Gammaproteobacteria	Betaproteobacteriales	Burkholderiaceae	Burkholderia-Caballeronia-Paraburkholderia	NA
OTU.3782	12C-Ami	meadow	Yes	6.986803	1.5342235	0.3496703	3.672670	0.0001200	0.0213026	2	Bacteria	Actinobacteria	Actinobacteria	Corynebacteriales	Nocardiaceae	Rhodococcus	NA
OTU.16149	12C-Ami	meadow	Yes	9.175487	1.7383960	0.3551714	4.190641	0.0000139	0.0028214	2	Bacteria	Proteobacteria	Gammaproteobacteria	Pseudomonadales	Pseudomonadaceae	Pseudomonas	Pseudomonas koreensis
OTU.2	12C-Xyl	agriculture	Yes	215.880615	1.1472726	0.2351432	3.815856	0.0000679	0.0389719	4	Bacteria	Proteobacteria	Gammaproteobacteria	Betaproteobacteriales	Burkholderiaceae	Burkholderia-Caballeronia-Paraburkholderia	NA
OTU.2	12C-Xyl	agriculture	Yes	249.987495	1.3658821	0.3072398	3.631958	0.0001406	0.0059671	2	Bacteria	Proteobacteria	Gammaproteobacteria	Betaproteobacteriales	Burkholderiaceae	Burkholderia-Caballeronia-Paraburkholderia	NA
OTU.2	12C-Xyl	agriculture	Yes	215.880615	1.1472726	0.2351432	3.815856	0.0000679	0.0389719	4	Bacteria	Proteobacteria	Gammaproteobacteria	Betaproteobacteriales	Burkholderiaceae	Burkholderia-Caballeronia-Paraburkholderia	NA
OTU.2	12C-Xyl	agriculture	Yes	249.987495	1.3658821	0.3072398	3.631958	0.0001406	0.0059671	2	Bacteria	Proteobacteria	Gammaproteobacteria	Betaproteobacteriales	Burkholderiaceae	Burkholderia-Caballeronia-Paraburkholderia	NA
OTU.66	12C-Xyl	agriculture	Yes	108.855465	1.8827157	0.2496612	6.539725	0.0000000	0.0000001	4	Bacteria	Proteobacteria	Gammaproteobacteria	Betaproteobacteriales	Burkholderiaceae	Burkholderia-Caballeronia-Paraburkholderia	NA
OTU.66	12C-Xyl	agriculture	Yes	124.839100	1.9549236	0.3147849	5.416154	0.0000000	0.0000030	2	Bacteria	Proteobacteria	Gammaproteobacteria	Betaproteobacteriales	Burkholderiaceae	Burkholderia-Caballeronia-Paraburkholderia	NA
OTU.66	12C-Xyl	agriculture	Yes	108.855465	1.8827157	0.2496612	6.539725	0.0000000	0.0000001	4	Bacteria	Proteobacteria	Gammaproteobacteria	Betaproteobacteriales	Burkholderiaceae	Burkholderia-Caballeronia-Paraburkholderia	NA
OTU.66	12C-Xyl	agriculture	Yes	124.839100	1.9549236	0.3147849	5.416154	0.0000000	0.0000030	2	Bacteria	Proteobacteria	Gammaproteobacteria	Betaproteobacteriales	Burkholderiaceae	Burkholderia-Caballeronia-Paraburkholderia	NA
OTU.3	12C-Van	meadow	Yes	367.006897	1.8751572	0.3784055	4.294750	0.0000087	0.0044072	4	Bacteria	Proteobacteria	Gammaproteobacteria	Pseudomonadales	Pseudomonadaceae	Pseudomonas	Ambiguous_taxa
OTU.3	12C-Van	meadow	Yes	598.496711	3.2789701	0.4011677	7.550383	0.0000000	0.0000000	2	Bacteria	Proteobacteria	Gammaproteobacteria	Pseudomonadales	Pseudomonadaceae	Pseudomonas	Ambiguous_taxa
OTU.3	12C-Van	meadow	Yes	367.006897	1.8751572	0.3784055	4.294750	0.0000087	0.0044072	4	Bacteria	Proteobacteria	Gammaproteobacteria	Pseudomonadales	Pseudomonadaceae	Pseudomonas	Ambiguous_taxa
OTU.3	12C-Van	meadow	Yes	598.496711	3.2789701	0.4011677	7.550383	0.0000000	0.0000000	2	Bacteria	Proteobacteria	Gammaproteobacteria	Pseudomonadales	Pseudomonadaceae	Pseudomonas	Ambiguous_taxa
OTU.3782	12C-Van	meadow	Yes	18.765526	2.3314754	0.3499449	5.948010	0.0000000	0.0000010	4	Bacteria	Actinobacteria	Actinobacteria	Corynebacteriales	Nocardiaceae	Rhodococcus	NA
OTU.3782	12C-Van	meadow	Yes	20.076912	3.0564123	0.3232447	8.682006	0.0000000	0.0000000	2	Bacteria	Actinobacteria	Actinobacteria	Corynebacteriales	Nocardiaceae	Rhodococcus	NA
OTU.3782	12C-Van	meadow	Yes	18.765526	2.3314754	0.3499449	5.948010	0.0000000	0.0000010	4	Bacteria	Actinobacteria	Actinobacteria	Corynebacteriales	Nocardiaceae	Rhodococcus	NA
OTU.3782	12C-Van	meadow	Yes	20.076912	3.0564123	0.3232447	8.682006	0.0000000	0.0000000	2	Bacteria	Actinobacteria	Actinobacteria	Corynebacteriales	Nocardiaceae	Rhodococcus	NA
OTU.1166	12C-Van	meadow	Yes	16.420254	1.6638561	0.2746476	5.147891	0.0000001	0.0000147	2	Bacteria	Actinobacteria	Actinobacteria	Corynebacteriales	Nocardiaceae	Rhodococcus	Ambiguous_taxa
OTU.2	12C-Ami	agriculture	Yes	460.160815	1.6191671	0.3311080	4.135108	0.0000177	0.0012530	4	Bacteria	Proteobacteria	Gammaproteobacteria	Betaproteobacteriales	Burkholderiaceae	Burkholderia-Caballeronia-Paraburkholderia	NA
OTU.2	12C-Ami	agriculture	Yes	546.506627	1.6335341	0.3685922	3.753563	0.0000872	0.0048597	2	Bacteria	Proteobacteria	Gammaproteobacteria	Betaproteobacteriales	Burkholderiaceae	Burkholderia-Caballeronia-Paraburkholderia	NA
OTU.2	12C-Ami	agriculture	Yes	460.160815	1.6191671	0.3311080	4.135108	0.0000177	0.0012530	4	Bacteria	Proteobacteria	Gammaproteobacteria	Betaproteobacteriales	Burkholderiaceae	Burkholderia-Caballeronia-Paraburkholderia	NA
OTU.2	12C-Ami	agriculture	Yes	546.506627	1.6335341	0.3685922	3.753563	0.0000872	0.0048597	2	Bacteria	Proteobacteria	Gammaproteobacteria	Betaproteobacteriales	Burkholderiaceae	Burkholderia-Caballeronia-Paraburkholderia	NA
OTU.66	12C-Ami	agriculture	Yes	179.139721	2.6579874	0.2494062	9.654884	0.0000000	0.0000000	4	Bacteria	Proteobacteria	Gammaproteobacteria	Betaproteobacteriales	Burkholderiaceae	Burkholderia-Caballeronia-Paraburkholderia	NA
OTU.66	12C-Ami	agriculture	Yes	210.384740	2.3425108	0.3797888	5.509670	0.0000000	0.0000017	2	Bacteria	Proteobacteria	Gammaproteobacteria	Betaproteobacteriales	Burkholderiaceae	Burkholderia-Caballeronia-Paraburkholderia	NA
OTU.66	12C-Ami	agriculture	Yes	179.139721	2.6579874	0.2494062	9.654884	0.0000000	0.0000000	4	Bacteria	Proteobacteria	Gammaproteobacteria	Betaproteobacteriales	Burkholderiaceae	Burkholderia-Caballeronia-Paraburkholderia	NA
OTU.66	12C-Ami	agriculture	Yes	210.384740	2.3425108	0.3797888	5.509670	0.0000000	0.0000017	2	Bacteria	Proteobacteria	Gammaproteobacteria	Betaproteobacteriales	Burkholderiaceae	Burkholderia-Caballeronia-Paraburkholderia	NA
OTU.193	12C-Ami	agriculture	Yes	72.464175	2.3130630	0.4535396	4.548804	0.0000027	0.0002005	2	Bacteria	Proteobacteria	Gammaproteobacteria	Betaproteobacteriales	Burkholderiaceae	Cupriavidus	Ambiguous_taxa
OTU.16149	12C-Ami	agriculture	Yes	21.593242	3.4617346	0.4060604	7.909500	0.0000000	0.0000000	4	Bacteria	Proteobacteria	Gammaproteobacteria	Pseudomonadales	Pseudomonadaceae	Pseudomonas	Pseudomonas koreensis
OTU.3782	12C-Ami	agriculture	Yes	12.433210	1.9041180	0.3626178	4.561601	0.0000025	0.0002191	4	Bacteria	Actinobacteria	Actinobacteria	Corynebacteriales	Nocardiaceae	Rhodococcus	NA
OTU.3782	12C-Ami	agriculture	Yes	14.552128	2.0661450	0.3948885	4.599134	0.0000021	0.0001774	2	Bacteria	Actinobacteria	Actinobacteria	Corynebacteriales	Nocardiaceae	Rhodococcus	NA
OTU.3782	12C-Ami	agriculture	Yes	12.433210	1.9041180	0.3626178	4.561601	0.0000025	0.0002191	4	Bacteria	Actinobacteria	Actinobacteria	Corynebacteriales	Nocardiaceae	Rhodococcus	NA
OTU.3782	12C-Ami	agriculture	Yes	14.552128	2.0661450	0.3948885	4.599134	0.0000021	0.0001774	2	Bacteria	Actinobacteria	Actinobacteria	Corynebacteriales	Nocardiaceae	Rhodococcus	NA
OTU.223	12C-Van	meadow	Yes	42.460611	1.9160213	0.4097038	4.066405	0.0000239	0.0019933	2	Bacteria	Proteobacteria	Alphaproteobacteria	Sphingomonadales	Sphingomonadaceae	Novosphingobium	uncultured bacterium
OTU.3	12C-Ami	forest	Yes	161.998275	2.7876475	0.3552902	7.142463	0.0000000	0.0000000	2	Bacteria	Proteobacteria	Gammaproteobacteria	Pseudomonadales	Pseudomonadaceae	Pseudomonas	Ambiguous_taxa
OTU.2	12C-Ami	forest	Yes	2911.058579	1.0187588	0.1898262	4.049804	0.0000256	0.0408034	4	Bacteria	Proteobacteria	Gammaproteobacteria	Betaproteobacteriales	Burkholderiaceae	Burkholderia-Caballeronia-Paraburkholderia	NA
OTU.9	12C-Ami	agriculture	Yes	1402.221020	2.2974544	0.2917992	7.016655	0.0000000	0.0000000	4	Bacteria	Actinobacteria	Actinobacteria	Micrococcales	Micrococcaceae	Ambiguous_taxa	Ambiguous_taxa
OTU.9	12C-Ami	agriculture	Yes	2031.842266	2.6071517	0.2382931	9.891816	0.0000000	0.0000000	2	Bacteria	Actinobacteria	Actinobacteria	Micrococcales	Micrococcaceae	Ambiguous_taxa	Ambiguous_taxa
OTU.9	12C-Ami	agriculture	Yes	1402.221020	2.2974544	0.2917992	7.016655	0.0000000	0.0000000	4	Bacteria	Actinobacteria	Actinobacteria	Micrococcales	Micrococcaceae	Ambiguous_taxa	Ambiguous_taxa
OTU.9	12C-Ami	agriculture	Yes	2031.842266	2.6071517	0.2382931	9.891816	0.0000000	0.0000000	2	Bacteria	Actinobacteria	Actinobacteria	Micrococcales	Micrococcaceae	Ambiguous_taxa	Ambiguous_taxa
OTU.315	12C-Ami	agriculture	Yes	392.139985	1.9600863	0.2492455	6.861052	0.0000000	0.0000000	4	Bacteria	Proteobacteria	Gammaproteobacteria	Betaproteobacteriales	Burkholderiaceae	Massilia	uncultured bacterium
OTU.315	12C-Ami	agriculture	Yes	535.845657	2.2230735	0.2927950	6.738753	0.0000000	0.0000000	2	Bacteria	Proteobacteria	Gammaproteobacteria	Betaproteobacteriales	Burkholderiaceae	Massilia	uncultured bacterium
OTU.315	12C-Ami	agriculture	Yes	392.139985	1.9600863	0.2492455	6.861052	0.0000000	0.0000000	4	Bacteria	Proteobacteria	Gammaproteobacteria	Betaproteobacteriales	Burkholderiaceae	Massilia	uncultured bacterium
OTU.315	12C-Ami	agriculture	Yes	535.845657	2.2230735	0.2927950	6.738753	0.0000000	0.0000000	2	Bacteria	Proteobacteria	Gammaproteobacteria	Betaproteobacteriales	Burkholderiaceae	Massilia	uncultured bacterium
OTU.23	12C-Ami	agriculture	Yes	93.608292	1.5439977	0.2326064	5.563036	0.0000000	0.0000015	4	Bacteria	Proteobacteria	Gammaproteobacteria	Betaproteobacteriales	Burkholderiaceae	Ralstonia	NA
OTU.20	12C-Ami	agriculture	Yes	757.867525	0.7834658	0.1667190	3.199790	0.0006876	0.0303748	2	Bacteria	Proteobacteria	Gammaproteobacteria	Betaproteobacteriales	Burkholderiaceae	Ambiguous_taxa	Ambiguous_taxa
OTU.463	12C-Ami	agriculture	Yes	148.253559	2.4180415	0.3429677	6.321416	0.0000000	0.0000000	4	Bacteria	Actinobacteria	Actinobacteria	Micrococcales	Micrococcaceae	Ambiguous_taxa	Ambiguous_taxa
OTU.463	12C-Ami	agriculture	Yes	245.424035	2.6656187	0.3248678	7.435699	0.0000000	0.0000000	2	Bacteria	Actinobacteria	Actinobacteria	Micrococcales	Micrococcaceae	Ambiguous_taxa	Ambiguous_taxa
OTU.463	12C-Ami	agriculture	Yes	148.253559	2.4180415	0.3429677	6.321416	0.0000000	0.0000000	4	Bacteria	Actinobacteria	Actinobacteria	Micrococcales	Micrococcaceae	Ambiguous_taxa	Ambiguous_taxa
OTU.463	12C-Ami	agriculture	Yes	245.424035	2.6656187	0.3248678	7.435699	0.0000000	0.0000000	2	Bacteria	Actinobacteria	Actinobacteria	Micrococcales	Micrococcaceae	Ambiguous_taxa	Ambiguous_taxa
OTU.1386	12C-Ami	agriculture	Yes	58.258922	1.2905180	0.2966817	3.507186	0.0002264	0.0125672	4	Bacteria	Proteobacteria	Gammaproteobacteria	Betaproteobacteriales	Burkholderiaceae	NA	NA
OTU.1386	12C-Ami	agriculture	Yes	66.543042	2.1527448	0.2855800	6.662739	0.0000000	0.0000000	2	Bacteria	Proteobacteria	Gammaproteobacteria	Betaproteobacteriales	Burkholderiaceae	NA	NA
OTU.1386	12C-Ami	agriculture	Yes	58.258922	1.2905180	0.2966817	3.507186	0.0002264	0.0125672	4	Bacteria	Proteobacteria	Gammaproteobacteria	Betaproteobacteriales	Burkholderiaceae	NA	NA
OTU.1386	12C-Ami	agriculture	Yes	66.543042	2.1527448	0.2855800	6.662739	0.0000000	0.0000000	2	Bacteria	Proteobacteria	Gammaproteobacteria	Betaproteobacteriales	Burkholderiaceae	NA	NA
OTU.2	12C-Xyl	forest	Yes	2283.299024	1.2753339	0.2374383	4.318317	0.0000079	0.0059549	4	Bacteria	Proteobacteria	Gammaproteobacteria	Betaproteobacteriales	Burkholderiaceae	Burkholderia-Caballeronia-Paraburkholderia	NA
OTU.1	12C-Xyl	forest	Yes	354.827089	0.9391310	0.1456613	4.731050	0.0000011	0.0016920	4	Bacteria	Proteobacteria	Gammaproteobacteria	Betaproteobacteriales	Burkholderiaceae	Burkholderia-Caballeronia-Paraburkholderia	Ambiguous_taxa
OTU.66	12C-Van	forest	Yes	64.385275	1.5741686	0.2865746	4.620677	0.0000019	0.0026750	2	Bacteria	Proteobacteria	Gammaproteobacteria	Betaproteobacteriales	Burkholderiaceae	Burkholderia-Caballeronia-Paraburkholderia	NA
OTU.2	12C-Van	forest	Yes	3957.520597	1.7175488	0.3265153	4.494579	0.0000035	0.0026750	2	Bacteria	Proteobacteria	Gammaproteobacteria	Betaproteobacteriales	Burkholderiaceae	Burkholderia-Caballeronia-Paraburkholderia	NA
OTU.2	12C-Van	forest	Yes	4835.350981	1.4233702	0.2440426	4.808054	0.0000008	0.0012177	4	Bacteria	Proteobacteria	Gammaproteobacteria	Betaproteobacteriales	Burkholderiaceae	Burkholderia-Caballeronia-Paraburkholderia	NA
OTU.2	12C-Van	forest	Yes	3957.520597	1.7175488	0.3265153	4.494579	0.0000035	0.0026750	2	Bacteria	Proteobacteria	Gammaproteobacteria	Betaproteobacteriales	Burkholderiaceae	Burkholderia-Caballeronia-Paraburkholderia	NA
OTU.2	12C-Van	forest	Yes	4835.350981	1.4233702	0.2440426	4.808054	0.0000008	0.0012177	4	Bacteria	Proteobacteria	Gammaproteobacteria	Betaproteobacteriales	Burkholderiaceae	Burkholderia-Caballeronia-Paraburkholderia	NA
OTU.951	12C-Xyl	meadow	Yes	23.722085	1.4350722	0.2565998	4.618367	0.0000019	0.0003055	2	Bacteria	Actinobacteria	Actinobacteria	Micrococcales	Microbacteriaceae	NA	NA
OTU.9	12C-Xyl	meadow	Yes	307.578454	1.5465536	0.2465028	5.259791	0.0000001	0.0000171	2	Bacteria	Actinobacteria	Actinobacteria	Micrococcales	Micrococcaceae	Ambiguous_taxa	Ambiguous_taxa
OTU.154	12C-Xyl	meadow	Yes	110.673703	1.4643399	0.2625814	4.624622	0.0000019	0.0003055	2	Bacteria	Actinobacteria	Actinobacteria	Micrococcales	Cellulomonadaceae	Cellulomonas	Ambiguous_taxa
OTU.2	12C-Xyl	meadow	Yes	180.311952	1.7821720	0.2239602	6.841270	0.0000000	0.0000000	2	Bacteria	Proteobacteria	Gammaproteobacteria	Betaproteobacteriales	Burkholderiaceae	Burkholderia-Caballeronia-Paraburkholderia	NA
OTU.66	12C-Xyl	meadow	Yes	28.053223	2.0078524	0.2912671	6.035190	0.0000000	0.0000003	2	Bacteria	Proteobacteria	Gammaproteobacteria	Betaproteobacteriales	Burkholderiaceae	Burkholderia-Caballeronia-Paraburkholderia	NA
OTU.2396	12C-Xyl	meadow	Yes	19.764453	1.6472424	0.3547402	3.938777	0.0000409	0.0055457	2	Bacteria	Proteobacteria	Gammaproteobacteria	Betaproteobacteriales	Burkholderiaceae	Massilia	NA
OTU.187	12C-Xyl	meadow	Yes	51.968729	1.6904341	0.2269908	6.345781	0.0000000	0.0000001	2	Bacteria	Proteobacteria	Alphaproteobacteria	Rhizobiales	Rhizobiaceae	Allorhizobium-Neorhizobium-Pararhizobium-Rhizobium	uncultured bacterium
OTU.2396	12C-Ami	agriculture	Yes	27.086295	1.8923076	0.3811749	4.308541	0.0000082	0.0006384	4	Bacteria	Proteobacteria	Gammaproteobacteria	Betaproteobacteriales	Burkholderiaceae	Massilia	NA
OTU.2396	12C-Ami	agriculture	Yes	43.956143	1.8111898	0.4107461	3.800863	0.0000721	0.0043848	2	Bacteria	Proteobacteria	Gammaproteobacteria	Betaproteobacteriales	Burkholderiaceae	Massilia	NA
OTU.2396	12C-Ami	agriculture	Yes	27.086295	1.8923076	0.3811749	4.308541	0.0000082	0.0006384	4	Bacteria	Proteobacteria	Gammaproteobacteria	Betaproteobacteriales	Burkholderiaceae	Massilia	NA
OTU.2396	12C-Ami	agriculture	Yes	43.956143	1.8111898	0.4107461	3.800863	0.0000721	0.0043848	2	Bacteria	Proteobacteria	Gammaproteobacteria	Betaproteobacteriales	Burkholderiaceae	Massilia	NA
OTU.8	12C-Van	agriculture	Yes	1850.903401	2.3176915	0.3966413	5.213001	0.0000001	0.0000388	4	Bacteria	Proteobacteria	Gammaproteobacteria	Betaproteobacteriales	Burkholderiaceae	Massilia	Ambiguous_taxa
OTU.2396	12C-Van	agriculture	Yes	90.659803	3.0985649	0.4914833	5.795852	0.0000000	0.0000028	4	Bacteria	Proteobacteria	Gammaproteobacteria	Betaproteobacteriales	Burkholderiaceae	Massilia	NA
OTU.2	12C-Van	agriculture	Yes	2180.028131	2.1732379	0.4791039	4.014240	0.0000298	0.0181100	2	Bacteria	Proteobacteria	Gammaproteobacteria	Betaproteobacteriales	Burkholderiaceae	Burkholderia-Caballeronia-Paraburkholderia	NA
OTU.2	12C-Van	agriculture	Yes	1069.948292	2.3885562	0.4823726	4.433412	0.0000046	0.0012908	4	Bacteria	Proteobacteria	Gammaproteobacteria	Betaproteobacteriales	Burkholderiaceae	Burkholderia-Caballeronia-Paraburkholderia	NA
OTU.2	12C-Van	agriculture	Yes	2180.028131	2.1732379	0.4791039	4.014240	0.0000298	0.0181100	2	Bacteria	Proteobacteria	Gammaproteobacteria	Betaproteobacteriales	Burkholderiaceae	Burkholderia-Caballeronia-Paraburkholderia	NA
OTU.2	12C-Van	agriculture	Yes	1069.948292	2.3885562	0.4823726	4.433412	0.0000046	0.0012908	4	Bacteria	Proteobacteria	Gammaproteobacteria	Betaproteobacteriales	Burkholderiaceae	Burkholderia-Caballeronia-Paraburkholderia	NA
OTU.1	12C-Van	agriculture	Yes	980.734255	2.6471368	0.5128590	4.674066	0.0000015	0.0013451	2	Bacteria	Proteobacteria	Gammaproteobacteria	Betaproteobacteriales	Burkholderiaceae	Burkholderia-Caballeronia-Paraburkholderia	Ambiguous_taxa
OTU.1	12C-Van	agriculture	Yes	589.750952	3.0252483	0.5020376	5.527970	0.0000000	0.0000090	4	Bacteria	Proteobacteria	Gammaproteobacteria	Betaproteobacteriales	Burkholderiaceae	Burkholderia-Caballeronia-Paraburkholderia	Ambiguous_taxa
OTU.1	12C-Van	agriculture	Yes	980.734255	2.6471368	0.5128590	4.674066	0.0000015	0.0013451	2	Bacteria	Proteobacteria	Gammaproteobacteria	Betaproteobacteriales	Burkholderiaceae	Burkholderia-Caballeronia-Paraburkholderia	Ambiguous_taxa
OTU.1	12C-Van	agriculture	Yes	589.750952	3.0252483	0.5020376	5.527970	0.0000000	0.0000090	4	Bacteria	Proteobacteria	Gammaproteobacteria	Betaproteobacteriales	Burkholderiaceae	Burkholderia-Caballeronia-Paraburkholderia	Ambiguous_taxa
OTU.8	12C-Xyl	agriculture	Yes	1616.901682	1.2408664	0.2885439	3.434023	0.0002973	0.0098445	2	Bacteria	Proteobacteria	Gammaproteobacteria	Betaproteobacteriales	Burkholderiaceae	Massilia	Ambiguous_taxa
OTU.131	12C-Pal	agriculture	Yes	43.772174	1.5132675	0.3099683	4.075473	0.0000230	0.0165366	28	Bacteria	Actinobacteria	Actinobacteria	Corynebacteriales	Nocardiaceae	Nocardia	NA
OTU.56	12C-Van	agriculture	Yes	193.226833	2.0932142	0.3779803	4.876482	0.0000005	0.0001804	4	Bacteria	Proteobacteria	Alphaproteobacteria	Rhizobiales	Beijerinckiaceae	Methylobacterium	uncultured bacterium
OTU.1541	12C-Xyl	agriculture	Yes	63.550455	1.1522105	0.2627946	3.433139	0.0002983	0.0098445	2	Bacteria	Actinobacteria	Actinobacteria	Micrococcales	Microbacteriaceae	Microbacterium	Ambiguous_taxa
OTU.9	12C-Xyl	agriculture	Yes	823.875478	1.4954986	0.1899400	6.557326	0.0000000	0.0000000	2	Bacteria	Actinobacteria	Actinobacteria	Micrococcales	Micrococcaceae	Ambiguous_taxa	Ambiguous_taxa
OTU.18	12C-Cel	agriculture	Yes	83.450918	1.4174655	0.2508977	4.653154	0.0000016	0.0032068	14	Bacteria	Proteobacteria	Gammaproteobacteria	Betaproteobacteriales	Burkholderiaceae	Duganella	NA
OTU.3	12C-Van	agriculture	Yes	1596.345820	3.3653851	0.4892176	6.368097	0.0000000	0.0000002	2	Bacteria	Proteobacteria	Gammaproteobacteria	Pseudomonadales	Pseudomonadaceae	Pseudomonas	Ambiguous_taxa
OTU.463	12C-Xyl	agriculture	No	57.143056	1.7055406	0.3470587	4.193932	0.0000137	0.0118094	4	Bacteria	Actinobacteria	Actinobacteria	Micrococcales	Micrococcaceae	Ambiguous_taxa	Ambiguous_taxa
OTU.463	12C-Xyl	agriculture	No	176.220818	2.3651105	0.3061382	6.909005	0.0000000	0.0000000	2	Bacteria	Actinobacteria	Actinobacteria	Micrococcales	Micrococcaceae	Ambiguous_taxa	Ambiguous_taxa
OTU.951	12C-Xyl	agriculture	No	84.136147	0.8679262	0.2059635	3.000173	0.0013491	0.0400692	2	Bacteria	Actinobacteria	Actinobacteria	Micrococcales	Microbacteriaceae	NA	NA
OTU.154	12C-Xyl	agriculture	No	83.888332	1.5065962	0.2482874	5.061054	0.0000002	0.0000155	2	Bacteria	Actinobacteria	Actinobacteria	Micrococcales	Cellulomonadaceae	Cellulomonas	Ambiguous_taxa
OTU.315	12C-Xyl	agriculture	No	477.770963	1.5345565	0.3461871	3.710585	0.0001034	0.0051178	2	Bacteria	Proteobacteria	Gammaproteobacteria	Betaproteobacteriales	Burkholderiaceae	Massilia	uncultured bacterium
OTU.187	12C-Xyl	agriculture	No	64.905291	1.6165815	0.2975099	4.593398	0.0000022	0.0001295	2	Bacteria	Proteobacteria	Alphaproteobacteria	Rhizobiales	Rhizobiaceae	Allorhizobium-Neorhizobium-Pararhizobium-Rhizobium	uncultured bacterium
OTU.8	12C-Ami	agriculture	No	1100.658842	1.6892232	0.2190543	6.570168	0.0000000	0.0000000	4	Bacteria	Proteobacteria	Gammaproteobacteria	Betaproteobacteriales	Burkholderiaceae	Massilia	Ambiguous_taxa
OTU.4461	12C-Ami	agriculture	No	24.743522	1.5908981	0.3615954	3.708283	0.0001043	0.0067557	4	Bacteria	Proteobacteria	Gammaproteobacteria	Betaproteobacteriales	Burkholderiaceae	NA	NA
OTU.7776	12C-Ami	agriculture	No	115.221961	0.8563077	0.1714353	3.536657	0.0002026	0.0121100	4	Bacteria	Proteobacteria	Gammaproteobacteria	Betaproteobacteriales	Burkholderiaceae	NA	NA
OTU.3	12C-Ami	agriculture	No	31.418578	2.3544258	0.4180531	5.033872	0.0000002	0.0000233	4	Bacteria	Proteobacteria	Gammaproteobacteria	Pseudomonadales	Pseudomonadaceae	Pseudomonas	Ambiguous_taxa
OTU.3589	12C-Ami	agriculture	No	14.800817	1.5392306	0.4049181	3.183929	0.0007265	0.0303748	2	Bacteria	Bacteroidetes	Bacteroidia	Chitinophagales	Chitinophagaceae	Flavisolibacter	NA
OTU.8	12C-Ami	agriculture	No	1912.137734	1.8269501	0.2647118	5.957233	0.0000000	0.0000001	2	Bacteria	Proteobacteria	Gammaproteobacteria	Betaproteobacteriales	Burkholderiaceae	Massilia	Ambiguous_taxa
OTU.2513	12C-Ami	agriculture	No	62.213661	1.1788555	0.2616336	3.550216	0.0001925	0.0091967	2	Bacteria	Proteobacteria	Gammaproteobacteria	Betaproteobacteriales	Burkholderiaceae	NA	NA
OTU.4461	12C-Ami	agriculture	No	32.142982	1.6209049	0.3337316	4.107807	0.0000200	0.0013361	2	Bacteria	Proteobacteria	Gammaproteobacteria	Betaproteobacteriales	Burkholderiaceae	NA	NA
OTU.3	12C-Ami	agriculture	No	113.241648	2.8100466	0.4301395	5.951666	0.0000000	0.0000001	2	Bacteria	Proteobacteria	Gammaproteobacteria	Pseudomonadales	Pseudomonadaceae	Pseudomonas	Ambiguous_taxa
OTU.711	12C-Ami	agriculture	No	32.047650	1.3359246	0.3043876	3.567572	0.0001802	0.0091967	2	Bacteria	Proteobacteria	Alphaproteobacteria	Rhizobiales	Beijerinckiaceae	Bosea	uncultured bacterium
OTU.187	12C-Ami	agriculture	No	53.188007	1.0761760	0.2623858	3.148707	0.0008200	0.0322683	2	Bacteria	Proteobacteria	Alphaproteobacteria	Rhizobiales	Rhizobiaceae	Allorhizobium-Neorhizobium-Pararhizobium-Rhizobium	uncultured bacterium
OTU.66	12C-Van	agriculture	No	211.157488	2.0560193	0.4797711	3.764335	0.0000835	0.0380326	2	Bacteria	Proteobacteria	Gammaproteobacteria	Betaproteobacteriales	Burkholderiaceae	Burkholderia-Caballeronia-Paraburkholderia	NA
OTU.66	12C-Van	agriculture	No	167.914591	2.7058574	0.3884788	6.321728	0.0000000	0.0000002	4	Bacteria	Proteobacteria	Gammaproteobacteria	Betaproteobacteriales	Burkholderiaceae	Burkholderia-Caballeronia-Paraburkholderia	NA
OTU.18	12C-Pal	agriculture	No	42.941360	1.4081744	0.2860221	4.049247	0.0000257	0.0165366	28	Bacteria	Proteobacteria	Gammaproteobacteria	Betaproteobacteriales	Burkholderiaceae	Duganella	NA
OTU.4420	12C-Pal	agriculture	No	113.862921	2.3114293	0.3187109	6.468023	0.0000000	0.0000001	28	Bacteria	Proteobacteria	Gammaproteobacteria	Betaproteobacteriales	Burkholderiaceae	Aquabacterium	NA
OTU.1863	12C-Ami	meadow	No	63.905406	1.7674692	0.3457300	4.389175	0.0000057	0.0013464	2	Bacteria	Proteobacteria	Gammaproteobacteria	Betaproteobacteriales	Burkholderiaceae	Massilia	NA
OTU.315	12C-Ami	meadow	No	168.680186	1.4360614	0.2688758	4.411186	0.0000051	0.0013464	2	Bacteria	Proteobacteria	Gammaproteobacteria	Betaproteobacteriales	Burkholderiaceae	Massilia	uncultured bacterium
OTU.66	12C-Van	meadow	No	45.834871	1.7014041	0.3599458	4.032285	0.0000276	0.0104398	4	Bacteria	Proteobacteria	Gammaproteobacteria	Betaproteobacteriales	Burkholderiaceae	Burkholderia-Caballeronia-Paraburkholderia	NA
OTU.2	12C-Van	meadow	No	828.031945	2.7981725	0.2536109	10.047569	0.0000000	0.0000000	4	Bacteria	Proteobacteria	Gammaproteobacteria	Betaproteobacteriales	Burkholderiaceae	Burkholderia-Caballeronia-Paraburkholderia	NA
OTU.29	12C-Van	meadow	No	23.315455	1.6340944	0.3734184	3.706551	0.0001051	0.0317673	4	Bacteria	Proteobacteria	Alphaproteobacteria	Sphingomonadales	Sphingomonadaceae	Sphingomonas	Ambiguous_taxa
OTU.85	12C-Van	meadow	No	50.025534	1.4205890	0.3140419	3.727494	0.0000967	0.0070973	2	Bacteria	Firmicutes	Bacilli	Bacillales	Bacillaceae	Bacillus	NA
OTU.35	12C-Van	meadow	No	83.736607	1.1173289	0.2567176	3.378533	0.0003644	0.0202832	2	Bacteria	Firmicutes	Bacilli	Bacillales	Bacillaceae	Bacillus	uncultured bacterium
OTU.2977	12C-Van	meadow	No	43.992439	1.4438190	0.3223333	3.703679	0.0001062	0.0070973	2	Bacteria	Firmicutes	Bacilli	Bacillales	Bacillaceae	Bacillus	Ambiguous_taxa
OTU.98	12C-Van	meadow	No	26.822922	1.5949465	0.3292538	4.084832	0.0000221	0.0019933	2	Bacteria	Firmicutes	Bacilli	Bacillales	Bacillaceae	Bacillus	Ambiguous_taxa
OTU.2396	12C-Van	meadow	No	50.033273	2.6333978	0.3383678	7.043808	0.0000000	0.0000000	2	Bacteria	Proteobacteria	Gammaproteobacteria	Betaproteobacteriales	Burkholderiaceae	Massilia	NA
OTU.66	12C-Van	meadow	No	46.853527	2.5619807	0.3194617	7.237114	0.0000000	0.0000000	2	Bacteria	Proteobacteria	Gammaproteobacteria	Betaproteobacteriales	Burkholderiaceae	Burkholderia-Caballeronia-Paraburkholderia	NA
OTU.2	12C-Van	meadow	No	818.088037	2.2775018	0.3727713	5.438996	0.0000000	0.0000036	2	Bacteria	Proteobacteria	Gammaproteobacteria	Betaproteobacteriales	Burkholderiaceae	Burkholderia-Caballeronia-Paraburkholderia	NA
OTU.16149	12C-Van	meadow	No	12.255583	1.6074319	0.4011831	3.383572	0.0003577	0.0202832	2	Bacteria	Proteobacteria	Gammaproteobacteria	Pseudomonadales	Pseudomonadaceae	Pseudomonas	Pseudomonas koreensis
OTU.9	12C-Ami	forest	No	279.245634	1.2881788	0.1957360	5.303976	0.0000001	0.0000441	2	Bacteria	Actinobacteria	Actinobacteria	Micrococcales	Micrococcaceae	Ambiguous_taxa	Ambiguous_taxa